Faculty of Arts & Science 2015-2016 Calendar

Computer Science

• Computer Science Specialist | Computer Science Specialist Focuses
• Computer Science Major | Computer Science Minor
• Computer Science Courses

Faculty

What is Computer Science?

Despite the name, Computer Science is not really a science of computers at all. Computers are quite remarkable electronic devices, but even more remarkable is what they can be made to do: simulate the flow of air over a wing, manage communication over the Internet, control the actions of a robot, synthesize realistic images, play grandmaster-level chess, and on and on. Indeed the application of computers in activities like these has affected most areas of modern life. What these tasks have in common has little to do with the physics or electronics of computers; what matters is that they can be formulated as some sort of computation. This is the real subject matter of Computer Science: computation, and what can or cannot be done computationally.

In trying to make sense of what we can get a computer to do, a wide variety of topics come up. There are, however, two recurring themes. The first is the issue of scale: how big a system can we specify without getting lost in the design, or how big a task can a computer handle within reasonable bounds of time, memory, and accuracy. A large part of Computer Science deals with these questions in one form or another. In the area of programming languages and methodology, for example, we look for notations for describing computations, and programming methodologies that facilitate the production of manageable and efficient software. In the theory of computation area, we study resource requirements in time and memory of many basic computational tasks.

The second theme concerns the scope of computation. Computers were originally conceived as purely numerical calculators, but today, we tend to view them much more broadly. Part of Computer Science is concerned with understanding just how far computational ideas can be applied. In the area of artificial intelligence, for example, we ask how much of the intelligent behaviour of people can be expressed in computational terms. In the area of human-computer interaction, we ask what sorts of normal day-to-day activities of people might be supported and augmented using computers.

Some Computer Science courses are offered in the evening, to allow part-time students to pursue our programs. Introductory courses and some higher-level courses are offered in the summer.

The Professional Experience Year Program (PEY) offers students the opportunity to gain valuable work experience in industry, over a twelve to sixteen-month period. It is available to eligible, full-time students. Students may also take advantage of the International Exchange Program offered by CIE.  Please refer to Student Services & Resources chapter of this Calendar.

Associate Chair - Undergraduate Studies: Senior Lecturer Francois PItt

Student Counsellors, Undergraduate Office: Bahen Building, 40 St. George Street, Rooms 4252/4254/4256, M5S 2E4 (416-978-6360, email: ug@cs.utoronto.ca).

Web site: www.cs.toronto.edu

Computer Science Programs

Tuition fees for students enrolled in Computer Science Specialist and Major programs are higher than those for most Arts and Science programs.  For more information visit www.fees.utoronto.ca .

This is a limited enrolment program that can only accommodate a limited number of students. Eligibility will be based on a student’s marks in the required courses. The precise mark thresholds outlined below are an estimate of what will be required in the coming POSt admission cycle. Achieving those marks does not necessarily guarantee admission to the POSt in any given year.

First set of criteria:

• An average mark of at least 67% in CSC148H1 and CSC165H1/CSC240H1 with a minimum mark of 60% in each
• Completion of 4.0 FCEs

Those who do not meet the first set of criteria will be considered using the second set of criteria:

• Completion of CSC148H1, CSC165H1, MAT136H1/MAT137Y1/MAT157Y1
• CSC207H1 and CSC236H1/CSC240H1 with an average grade of at least 70%
• Completion of 4.0 FCEs

Note that students admitted to the program after second or third year will be required to pay retroactive program fees.

(12.0 full course equivalents [FCEs], including at least 1.5 FCEs at the 400-level)

First year (2.5 FCEs):
 1. (CSC108H1, CSC148H1)/CSC150H1, CSC165H1/CSC240H1; (MAT135H1, MAT136H1)/MAT137Y1/MAT157Y1

Second year (3.5 FCEs):
 2. CSC207H1, CSC209H1, CSC236H1/CSC240H1, CSC258H1, CSC263H1/CSC265H1; MAT221H1/MAT223H1/MAT240H1; STA247H1/STA255H1/STA257H1

Notes:

1. Students with a strong background in an object-oriented language such as Python, Java or C++ may omit CSC108H1 and proceed directly with CSC148H1. [There is no need to replace the missing half-credit; however, please base your course choice on what you are ready to take, not on “saving” a half-credit].
2. CSC240H1 is an accelerated and enriched version of CSC165H1 plus CSC236H1, intended for students with a strong mathematical background, or who develop an interest after taking CSC165H1. If you take CSC240H1 without CSC165H1, there is no need to replace the missing half-credit; but please see Note 1.
3. Consult the Undergraduate Office for advice about choosing among CSC108H1 and CSC148H1, and between CSC165H1 and CSC240H1.

Later years (6.0 FCEs):
3.  CSC369H1, CSC373H1/CSC375H1

4. 5.0 FCEs from the following: 

• Any 300-/400-level CSC course;
• BCB410H1, BCB420H1, BCB430Y1;
• ECE385H1, ECE489H1;
• MAT224H1, MAT235Y1/MAT237Y1/MAT257Y1, any 300-/400-level MAT course except MAT329Y1, MAT390H1, MAT391H1;
• STA248H1/STA261H1, any 300-/400-level STA course

with at most 2.0 FCEs from MAT or STA courses, and at least 1.5 FCEs from 400-level CSC, BCB, or ECE courses.

No more than 1.0 FCE from CSC490H1, CSC491H1, CSC494H1, CSC495H1, BCB430Y1 may be used to fulfill program requirements

The choices in 4 must satisfy the requirement for an integrative, inquiry-based activity by including one of the following half-courses: CSC301H1, CSC318H1, CSC404H1, CSC411H1, CSC418H1, CSC420H1, CSC428H1, CSC454H1, CSC485H1, CSC490H1, CSC491H1, CSC494H1, CSC495H1.  This requirement may also be met by participating in the PEY (Professional Experience Year) program. 

Preparing for graduate study in Computer Science

Strong students should consider the option of further study in graduate school (where the degrees offered are typically M.Sc. and Ph.D.). If you find yourself frequently receiving marks in the B+ range or better, you should consult with faculty members to learn more about graduate school and whether it would be a good option for you. You will want to ask for advice on your particular interests—and you will find faculty members are happy to talk to you—but there are also some course choices that should be considered by all students thinking of graduate study in Computer Science.

The focuses can help you further refine your areas of interest, but you should not take courses exclusively in one area. You will benefit by having taken an advanced course requiring considerable software development and a theory course.

It will be especially beneficial to have done a project course (CSC494H1/CSC495H1), a capstone course (CSC490H1/CSC491H1), and/or a summer research project. It is good if this individual work is in the area where you eventually decide you'd like to do your own research, but that is not essential; what you need most is some experience doing work on your own, under the mentorship of an experienced researcher.

Choosing courses

This program offers considerable freedom to choose courses at the 300-/400-level, and you are free to make those choices on your own. We are eager to offer guidance, however, and both our Undergraduate Office and individual faculty members are a rich source of advice.

You have the option of completing one or more of the focuses defined below. Focuses are sets of courses that direct you toward expertise in particular areas of Computer Science, such as game design, theory of computation, human-computer interaction. These focuses are meant to guide your course selection, not to constrain it. Each focus has at least one faculty member who would be happy to discuss the focus with you.

More information about each of the focuses can be found on our web site at http://web.cs.toronto.edu/program/ugrad/programs.htm

Each focus has a set of required courses that must be completed to satisfy the focus. Most focuses also have an additional list of related courses that students in the focus may find interesting. In some cases these are courses offered by different departments or faculties. Note that you must petition to take Engineering courses or graduate-level courses.

In many cases, the courses required for the focus will also satisfy Specialist program requirements. Focuses that require courses in addition the Specialist requirements have a note in the descriptions below.

To enrol in one or more focuses, students must first be enrolled in the Computer Science Specialist program. Enrolment instructions can be found on the Arts & Science Current Students subject POSt enrolment web site. Focuses can be chosen on ROSI after admission to the program.

Focus in Scientific Computing (3.5 FCEs)

Scientific computing studies the world around us. Known and unknown quantities are related through certain rules, e.g. physical laws, formulating mathematical problems. These problems are solved by numerical methods implemented as algorithms and run on computers. The numerical methods are analyzed and their performance (e.g. accuracy, efficiency) studied. Problems, such as choosing the optimal shape for an airplane (to achieve, for example, minimal fuel consumption), finding the fair price for derivative products of the market, or regulating the amount of radiation in medical scans, can be modelled by mathematical expressions, and solved by numerical techniques.

Students wishing to study scientific computing should have a strong background in mathematics—in particular calculus of several variables, linear algebra, and statistics—be fluent in programming, and have a good understanding of data structures and algorithm design.

Required Courses:

     1. MAT235Y1/MAT237Y1/MAT257Y1,
     2. 1.5 FCEs from the following: CSC336H1, CSC436H1, CSC446H1, CSC456H1, CSC466H1
     3. 1.0 FCE from the following: CSC320H1/CSC418H1, CSC321H1/CSC411H1, CSC343H1, CSC384H1,  CSC358H1/CSC458H1

Suggested Related Courses:

MAT224H1/MAT240H1, MAT244H1, MAT334H1/MAT354H1, MAT337H1/MAT357H1
It is also recommended that students in this focus consider taking a half-course or two from the basic sciences (such as physics, chemistry, biology), as these sciences are the source of many problems solved by numerical techniques.

Focus in Artificial Intelligence (3.5 FCEs)

Artificial Intelligence (AI) is aimed at understanding and replicating the computational processes underlying intelligent behaviour. These behaviours include the perception of one's environment, learning how that environment is structured, communicating with other agents, and reasoning to guide one's actions. This focus is designed to provide students with an introduction to some of the key scientific and technical ideas that have been developed in AI. There are four different sub-areas of AI represented in our department: Computer Vision, Computational Linguistics (CL), Machine Learning (ML), and Knowledge Representation and Reasoning (KR). These areas cover a wide variety of ideas and techniques. Students wanting to achieve this focus are required to take courses from at least two of these sub-areas.

Required Courses:

     1. 1.0 FCE from the following: MAT235Y1/MAT237Y1/MAT257Y1, APM236H1/MIE262H1/STA248H1/STA261H1, CSC336H1, CSC310H1, CSC330H1, CSC438H1, CSC448H1, CSC463H1
     2. 2.5 FCEs from the following covering at least two of the four areas
              a) CSC401H1, CSC485H1
              b) CSC320H1, CSC420H1
              c) CSC321H1, CSC411H1, CSC412H1
              d) CSC384H1, CSC486H1

Suggested Related Courses:

CSC200Y1, CSC324H1, COG250Y1, PSY270H1, PHL232H1, PHL342H1, STA414H1

Focus in Computational Linguistics & Natural Language Processing (4.0 FCEs)

How can we build and analyze systems that enable users to communicate with computers using human language (also called natural language), and for automatically processing the vast amounts of data on the web available in the form of text? The focus covers appropriate material on natural language interfaces, as well as tools such as document summarization, intelligent search over the web, and so on. Students considering this focus are encouraged to consider a second Major in Linguistics. [Note 0.5 FCE in LIN is in addition to the 12.0 FCEs required to complete the Specialist program]

Required Courses

     1.     CSC318H1
     2.     CSC401H1, CSC485H1
     3.     LIN100Y1/LIN200H1
     4.     1.5 FCEs from the following: CSC309H1, CSC321H1, CSC330H1, CSC411H1, CSC428H1, CSC486H1         
     5.     0.5 FCE from the following: PSY100H1, COG250Y1

Suggested Related Courses:

Other relevant Computer Science courses, depending on the student's interests, include other courses in artificial intelligence such as CSC384H1 or CSC420H1. Linguistics, Psychology, and Cognitive Science are all directly relevant to this focus, and we recommend that interested students take additional courses from any or all of those disciplines.

Focus in Computer Vision  (3.5 FCEs)

Computer vision is the science and technology of machines that can see. As a science, the goal of computer vision is to understand the computational processes required for a machine to come to an understanding of the content of a set of images. The data here may be a single snapshot, a video sequence, or a set of images from different viewpoints or provided by medical scanners.

The computer vision focus introduces students to the study of vision from a computational point of view. That is, we attempt to clearly define computational problems for various steps of the overall process, and then show how these problems can be tackled with appropriate algorithms.

Students who wish to pursue computer vision should have an understanding of linear algebra and calculus of several variables. Moreover, they should be solid programmers and have a good understanding of data structures and algorithm design. These basic tools are required in order to first pose computational vision problems, and then develop and test algorithms for the solution to those problems.

Required Courses:

     1. MAT235Y1/MAT237Y1/MAT257Y1, CSC320H1, CSC336H1, CSC411H1, CSC420H1
     2. 0.5 FCE from the following: CSC418H1, CSC412H1, CSC2503H (Note: students must petition to take a graduate course.)

Suggested Related Courses:

The following are examples of topics and courses that fit naturally with a study of computational vision. The list is meant to be illustrative of the range of cognate topics, but is not necessarily complete. The ordering is alphabetical and not indicative of importance. Note: there are prerequisites for many of these courses that we do not list here.

APM462H1, COG250Y1, CSC384H1, CSC485H1, CSC486H1, ECE216H1, PHL232H1, PHY385H1, PSL440Y1, PSY270H1, PSY280H1, STA257H1/STA261H1

Focus in Computer Systems (3.5 FCEs)

Software systems are complex and interesting. Poorly done systems can be incredibly expensive: they can cost society billions of dollars, and sometimes make the difference between life and death. Rapid changes in technology and applications means that the underlying systems must continually adapt. This focus takes you under the covers of software systems, laying bare the layers and introducing you to concurrency issues, scalability, multiprocessor systems, distributed computing, and more.

Required Courses:

     1. CSC324H1, CSC343H1, CSC443H1, CSC469H1, CSC488H1
     2. 1.0 FCE from the following: CSC372H1/ECE385H1, CSC358H1, CSC458H1

Suggested Related Courses:

     1. CSC301H1, CSC309H1, CSC410H1, ECE489H1
     2. Relevant courses offered at UTM: CSC347H5, CSC423H5, CSC427H5
     3. Relevant courses offered by Engineering: ECE454H1, ECE568H1

Focus in Game Design (3.5 FCEs):

Video game design combines several disciplines within computer science, including software engineering, graphics, artificial intelligence, and human-computer interaction. It also incorporates elements of economics, psychology, music, and creative writing, requiring video game researchers to have a diverse, multidisciplinary set of skills.

Students who wish to pursue video game design should have an understanding of linear algebra (for computer graphics modeling), computer hardware and operating systems (for console architecture), data structures, and algorithm design. Students will gain a general knowledge of the more advanced topics listed in the courses below.

Required courses:

     1. CSC300H1, CSC301H1, CSC318H1, CSC324H1, CSC384H1, CSC418H1, CSC404H1

Suggested Related Courses:

     1. CSC358H1, CSC458H1, CSC428H1,
     2. MUS300H1, CIN212H1/INI222H1, CIN432H1/INI465H1, ENG235H1
     3. ECO326H1, RSM482H1/MGT2056H

Focus in Human-Computer Interaction (6.5 FCEs)

Human-Computer Interaction (HCI) is the scientific study of the use of computers by people and the design discipline that informs the creation of systems and software that are useful, usable, and enjoyable for the people who use them. HCI students have exciting opportunities for research and graduate school; HCI professionals often have jobs with titles such as user interface architect, user interface specialist, interaction designer, or usability engineer. [Note 3.5 FCEs in SOC & PSY are in addition to the 12.0 FCEs required to complete the Specialist program]

Required Courses:

     1. CSC300H1, CSC301H1, CSC318H1, CSC428H1
     2. SOC101Y1, SOC200H1, SOC202H1, SOC302H1 [To enrol in restricted SOC courses, please contact the CS Undergraduate Office in the July preceeding the academic year in which you plan to take the course]
     3. 1.0 FCE from the following: CSC309H1, CSC320H1, CSC321H1, CSC343H1, CSC384H1, CSC401H1, CSC404H1, CSC418H1, CSC485H1, CSC490H1/491H1
     4. PSY100H1, PSY270H1/PSY280H1

Suggested Related Courses:

     1. CSC454H1, CSC290H1
     2. At least one half-course in Human Factors or Ergonomics offered by the Department of Mechanical and Industrial Engineering, such as MIE240H1, MIE343H1, MIE344H1, MIE448H1, or MIE449H1. Human factors is a sister discipline to human-computer interaction that approaches problems in slightly different ways.
     3. WDW260H1

Focus in Theory of Computation (4.5 FCEs + 2.0 FCEs from required Specialist courses)

Why is it easy to sort a list of numbers, but hard to break Internet encryption schemes? Is finding a solution to a problem harder than checking that a solution is correct? Can we find good approximate solutions, even when the exact solutions seem out of reach? Theory of Computation studies the inherent complexity of fundamental algorithmic problems. On one hand, we develop ground-breaking efficient data structures and algorithms. On the other, we have yet to develop good algorithms for many problems despite decades of effort, and for these problems we strive to prove no time- or space-efficient algorithms will ever solve them. While the field has seen some successful impossibility results, there are still many problems—such as those underlying modern cryptography and security—for which we do not know either efficient algorithms or strong lower bounds!

This focus takes a rigorous, mathematical approach to computational problem-solving: students will gain a deep understanding of algorithm paradigms and measures of problem complexity, and develop the skills necessary to convey abstract ideas with precision and clarity. Many of our students go on to graduate studies and sophisticated algorithmic work in industry. This focus has natural ties with many branches of mathematics and is the foundation of many computer science fields. Consequently, our students often apply their theoretical knowledge to other fields of interest.

We strongly encourage taking the enriched theory courses (CSC240H1, CSC265H1) as well as specialist/major versions of the MAT requirements for our focus. [Depending on courses selected for points 4 & 5, students may need to complete 0.5–1.0 FCE in addition to the 12.0 FCEs required to complete the Specialist program.]

Required Courses:

     1.     MAT137Y1/MAT157Y1/MAT237Y1 (Note: If MAT237Y1 is used here, it cannot be counted as part of the 2.0 FCEs for point 5, below.)
     2.     CSC463H1
     3.     CSC336H1/CSC350H1
     4.     1.5 FCEs from the following: CSC310H1, CSC438H1, CSC448H1, MAT443H1, MAT332H1, MAT344H1, At UTM: CSC322H5/MAT302H5, CSC422H5; CSC494H1/CSC495H1 project supervised by a faculty member from the Theory group, or a relevant introductory graduate course in Theory. (Note that students must petition to take a graduate course.)        
     5.     2.0 FCEs from the following: APM236H1/MIE262H1, MIE263H1, APM421H1, APM461H1, MAT224H1/MAT247H1, MAT237Y1/MAT257Y1, MAT244H1/MAT267H1, MAT301H1/MAT347Y1, MAT315H1, MAT327H1, MAT334H1/MAT354H1, MAT337H1/MAT357H1, any 400-level MAT course (except MAT443H1), STA248H1/STA261H1, STA347H1

Recommended Courses:

     1.     Students are strongly encouraged to take the enriched theory courses: CSC240H1 and CSC265H1, rather than their regular counterparts: CSC165H1/CSC236H1 and CSC263H1, respectively.

Suggested Related Courses:

     1. BCB410H1
     2. CSC320H1/CSC418H1/CSC420H1, CSC321H1/CSC384H1/CSC411H1/CSC485H1, CSC343H1/CSC443H1, CSC351H1/CSC456H1, CSC358H1/CSC458H1, CSC412H1/CSC465H1/CSC486H1, CSC488H1

Focus in Web and Internet Technologies (3.5 FCEs)

The Web and Internet Technologies focus introduces students to the systems and algorithms that power today's large-scale web and Internet applications such as search engines, social networking applications, web data mining applications, and content distribution networks. The focus covers the algorithm foundations of web and internet technologies, as well as implementation and system architecture.

Students who wish to pursue the Focus in Web and Internet Technologies should have a solid understanding of statistics, should be good programmers and have a good understanding of data structures and algorithm design.

To get practical experience, students pursuing the web and Internet technologies focus are encouraged to do either a term project or a summer USRA project in web and internet technologies.

Required courses:

     1. STA248H1, CSC309H1, CSC343H1, CSC358H1, CSC458H1, CSC411H1
     2. 0.5 FCE from the following: CSC310H1, CSC443H1, CSC469H1

Suggested Related Courses:

     1. Courses offered at UTM: CSC347H5, CSC423H5, CSC427H5
     2. ECE568H1

This is a limited enrolment program that can only accommodate a limited number of students. Eligibility will be based on a student’s marks in the required courses. The precise mark thresholds outlined below are an estimate of what will be required in the coming POSt admission cycle. Achieving those marks does not necessarily guarantee admission to the POSt in any given year.

Applying using the first set of criteria:

• An average mark of at least 67% in CSC148H1 and CSC165H1/CSC240H1 with a minimum mark of 60% in each course
• Completion of 4.0 FCEs

Those who do not meet the first set of criteria will be considered using the second set of criteria:

• Completion of CSC148H1, CSC165H1, MAT136H1/MAT137Y1/MAT157Y1
• CSC207H1 and CSC236H1/CSC240H1 with an average grade of at least 70%
• Completion of 4.0 FCEs  

Note that students admitted to the program after second or third year will be required to pay retroactive program fees.

(8.0 full course equivalents [FCEs], including at least 0.5 FCE at the 400-level)

First year (2.5 FCEs):
  1. (CSC108H1, CSC148H1)/CSC150H1, CSC165H1/CSC240H1; (MAT135H1, MAT136H1)/MAT137Y1/MAT157Y1
Second year (2.5 FCEs):
  2. CSC207H1, CSC236H1/CSC240H1, CSC258H1, CSC263H1/CSC265H1; STA247H1/STA255H1/STA257H1

Notes:
1. Students with a strong background in an object-oriented language such as Python, Java or C++ may omit CSC108H1 and proceed directly with CSC148H1. [There is no need to replace the missing half-credit; however, please base your course choice on what you are ready to take, not on “saving” a half-credit].
2. CSC240H1 is an accelerated and enriched version of CSC165H1 plus CSC236H1, intended for students with a strong mathematical background, or who develop an interest after taking CSC165H1. If you take CSC240H without CSC165H1, there is no need to replace the missing half-credit; but please see Note 1.
3. Consult the Undergraduate Office for advice about choosing among CSC108H1 and CSC148H1, and between CSC165H1 and CSC240H1.

Later years (3.0 FCEs):
 3.  3.0 FCEs from the following:

• Any 200-/300-/400-level CSC course;
• BCB410H1, BCB420H1, BCB430Y1; 
• ECE385H1, ECE489H1;
• MAT221H1/MAT223H1/MAT240H1, MAT235Y1/MAT237Y1/MAT257Y1, any 300-/400-level MAT course except MAT329Y1, MAT390H1, MAT391H1

with at least 0.5 FCE from a 400-level CSC/BCB course, at least 1.0 additional FCE from 300-/400-level CSC/BCB/ECE courses, and at least 0.5 additional FCE from a 300-/400-level course.

No more than more than 1.0 FCE from CSC490H1, CSC491H1, CSC494H1, CSC495H1, BCB430Y1 may be used to fulfill program requirements.

The choices in 3 must satisfy the requirement for an integrative, inquiry-based activity by including one of the following half-courses: CSC301H1, CSC318H1, CSC404H1, CSC411H1, CSC418H1, CSC420H1, CSC428H1, CSC454H1, CSC485H1, CSC490H1, CSC491H1, CSC494H1, CSC495H1.  This requirement may also be met by participating in the PEY (Professional Experience Year) program.

A Major program in any discipline may form part (but not the whole) of your degree requirements. The Major program in Computer Science is designed to include a solid grounding in the essentials of Computer Science, followed by options that let you explore one or a few topics more deeply. You will also realize what areas you have not studied, and be ready to explore them if your interests change after completing the Major.

To give you freedom to choose your path through Computer Science, we have designed the Major to include a minimal set of required courses. There are some courses that we think you ought to consider carefully as you make those choices. CSC373H1 is fundamental to many more advanced Computer Science topics, where designing appropriate algorithms is central. CSC209H1 is a prerequisite to effective work in many application areas.

We have designed “packages” of related courses that are intended to accompany the Specialist program in Computer Science, and you may find them helpful in completing your Major too. Please see our web site at http://web.cs.toronto.edu/program/ugrad.htm

A significant role of the Major is to allow you to integrate your studies in Computer Science and another discipline. For example, many Computer Science students are also interested in statistics, economics, physics or mathematics. In those cases, it makes sense to enrol in a Major in one discipline and either a Major or a Specialist in the other. If your interests are evenly balanced, the obvious choice is to do two Majors, and that is what we assume here.

If you are doing a double Major (two Majors in related disciplines), you might want to consult your college registrar’s office for advice on satisfying the degree requirements with overlapping Majors. A number of sample combinations are listed below for your reference. This is not a complete list: many other combinations are possible.

A Major program is generally not enough to prepare you for graduate study in Computer Science, though a complete Specialist is not required. Please consult the advice about graduate study included with the description of the Specialist program in Computer Science.

CSC and Mathematics
The theoretical foundations of Computer Science are essentially a branch of mathematics, and numerical analysis (the area of CS that studies efficient, reliable and accurate algorithms for the numerical solution of continuous mathematical problems) is also a topic in applied mathematics. If you are interested in both Computer Science and Mathematics, a double major is a good choice.

In this double major, you should choose all the theoretical courses in the first three years: CSC165H1, CSC236H1, CSC263H1, CSC373H1, and CSC463H1. If the "enriched" versions are available as alternatives, you should prefer them: CSC240H1 in place of CSC165H1 and CSC236H1, and CSC265H1 in place of CSC263H1. You should also take at least one of CSC438H1, CSC448H1, and CSC465H1.

You should also make sure you take courses in numerical analysis -- CSC336H1 and CSC436H1, and possibly CSC446H1.

In the Major in Mathematics, you should prefer courses that are also in the Specialist program in Mathematics: MAT157Y1, MAT240H1, MAT247H1 and so on. Ask the advisors in the Department of Mathematics which courses they would recommend if you're planning a career in mathematics. Don't be afraid to admit your interest in CS.

CSC and Bioinformatics/Computational Biology
Bioinformatics is a field that came into existence only in the 1990s but has become an extremely fruitful interaction between biological scientists and computer scientists. Deciphering the genome requires not just extremely clever biology but extremely clever computer science, drawing from the study of algorithms and data structures and from data mining.

To study bioinformatics, you should enrol in the Specialist program in Bioinformatics and Computational Biology sponsored by the Department of Biochemistry, and also in the Major in Computer Science. Your Computer Science Major should include a selection of courses something like this:

  CSC373H1/CSC375H1
  CSC321H1/CSC343H1
  CSC336H1/CSC350H1
  BCB410H1, BCB420H1
  CSC411H1
  Some of CSC310H1, CSC324H1, CSC412H1, CSC456H1, CSC463H1

You should seek advice from both the Department of Biochemistry and the Department of Computer Science on how to distribute your courses across the two programs.

CSC and Statistics
Here your Computer Science course choices should be somewhat similar to those for Computer Science and Mathematics: take the theoretical Computer Science courses up to the 300-level, and prefer the higher-level MAT and STA courses. For example, take STA257H1 and STA261H1 rather than STA247H1 and STA248H1.

Within Computer Science, take courses in numerical analysis (CSC336H1 and CSC436H1). Choose also from among information theory (CSC310H1), machine learning (CSC321H1 and CSC411H1), and natural language processing (CSC401H1).

CSC and Economics
There is considerable opportunity for mutually supporting interests in Computer Science and economics in the area of economic modelling, econometrics, and numerical analysis. In Computer Science, you might choose courses such as CSC343H1 (databases), CSC358H1 (networks), and CSC369H1 (operating systems) to acquire the technical background for working with large systems and data sets, and CSC336H1 and CSC436H1 (numerical analysis) to understand the difficulties of large numerical models.

If you are interested in financial modelling, you will also want to take CSC446H1 to learn how to handle partial differential equations; to do that, you would want to have taken the necessary mathematical courses.

Applying ideas from economics to Computer Science is a little harder, but certainly economic principles apply to databases (CSC443H1) and networks (CSC458H1). CSC358H1 discusses how to model the processes involved in computer networks and in other customer-server systems. CSC454H1 (Business of Software) would also benefit from your background in economics.

CSC and Linguistics
If you are interested in both Computer Science and Linguistics, you should consider doing a Major in both. Your Major in Computer Science should focus on computational linguistics (CL), the sub-area of AI concerned with human languages (“natural languages”); researchers in this area are interested in developing programs that can “understand” and generate natural language. You should take our Computational Linguistics courses, CSC401H1 and CSC485H1. (They can be taken in either order.) As preparation, you should also take CSC324H1 (programming languages). Other courses you might find valuable are CSC384H1 (AI), CSC343H1 (databases), and the theoretical courses CSC373H1/CSC375H1 and CSC463H1.

CSC and Physics
If you want to study Computer Science and physics, then as a physicist, you will be interested in how natural processes and human design can take us from the materials and laws of nature to useful computational machinery, and you will want to study CSC258H1 (computer organization—the way solid-state devices can be combined to build a machine that repeatedly executes instructions) and CSC369H1 (operating systems—the large software systems that organize the programs people write and run to present the appearance of a well-run self-policing machine).

As a computer scientist, you will wonder how accurately you can compute the results of calculations needed in simulating or predicting physical processes. CSC336H1 and CSC436H1 introduce you to numerical analysis, and CSC446H1 applies it to partial differential equations, used to model many physical systems.

Both a computer scientist and a physicist will wonder how to write effective programs. CSC263H1 and CSC373H1 teach you to choose appropriate data structures and algorithms, and CSC463H1 helps you to understand whether a problem is computable, and if so, whether the computation takes a reasonable amount of time.

In fourth year, you may choose CSC418H1, which depends on and also simulates the behaviour of light and mechanical systems. CSC456H1 deals with high-performance computing of the kind used in scientific computing. CSC420H1 might also be a good choice, though some preparation in artificial intelligence would be helpful for this course.

This is a limited enrolment program that can only accommodate a limited number of students.  Eligibility will be based on a student’s marks in the required courses.  The precise mark thresholds outlined below are an estimate of what will be required in the coming POSt admission cycle. Achieving those marks does not necessarily guarantee admission to the POSt in any given year.

Applying using the first set of criteria:

• An average mark of at least 75% in CSC148H1 and CSC165H1/CSC240H1.
• Completion of 4.0 FCEs

Those who do not meet the first set of criteria will be considered using the second set of criteria:

• Completion of CSC148H1, CSC165H1/CSC240H1
• CSC207H1 and CSC236H1/CSC240H1 with an average mark of 75%
• Completion of 4.0 FCEs

(4.0 full course equivalents [FCEs])

1. (CSC108H1, CSC148H1)/CSC150H1, CSC165H1/CSC240H1

2. CSC207H1, CSC236H1/CSC240H1

Notes:
1. Students with a strong background in Java or C++ may omit CSC108H1 and proceed directly with CSC148H1.
2. CSC240H1 is an accelerated and enriched version of CSC165H1 plus CSC236H1, intended for students with a strong mathematical background, or who develop an interest after taking CSC165H1.
3. Consult the Undergraduate Office for advice about choosing among CSC108H1 and CSC148H1, and between CSC165H1 and CSC240H1.

(Total of above requirements: 2.5 FCEs. If you take fewer than 2.5 FCEs, you must take more than 1.5 FCEs from the next list, so that the total is 4.0 FCEs.)

3. 1.5 FCEs from the following list, of which at least 1.0 FCE must be at the 300-/400-level:
  CSC: any 200-/300-/400-level

Note:
1. Computer Science Minors are limited to three 300-/400-level CSC/ECE half-courses

Computer Science Courses

1. The University of Toronto Mississauga and University of Toronto Scarborough Computer Science Minor subject POSt is not recognized as a restricted Computer Science subject POSt for St. George course enrolments.

2. No late registration is permitted in any Computer Science course after the first two weeks of classes.  Enrolment in most Computer Science courses above 100-level MAY be restricted. Consult the Calendar or the Arts & Science Registration Instructions and Timetable for details.

Prerequisites and exclusions for all courses are strictly enforced. Prerequisite waivers can be granted by instructors if the student demonstrates that s/he has background covering the material of the prerequisite course(s). 

Please refer to the Arts & Science Registration Instructions and Timetable for prerequisite waiver deadlines.

Students may go to their college to drop down from enriched courses to regular courses. The courses are as follows: from CSC148H1 to CSC108H1, from CSC240H1 to CSC165H1 (or to CSC236H1 if you have already passed CSC165H1 with at least 60%), and from CSC265H1 to CSC263H1.  Students may only drop down if there is space in the course into which they are moving.

Drop down deadlines:
20159, Fall session: October 2, 2015
20161, Winter session January 29, 2016

If you have transfer credits in Computer Science, or a similar subject, for courses done at another university or college, contact our Undergraduate Office (BA4252/4254) for advice on choosing courses. Also ask for advice even if you don’t have transfer credits yet but are considering degree study at the University of Toronto. Without advice, you risk poor course choice or other adverse consequences.

The 199Y1 and 199H1 seminars are designed to provide the opportunity to work closely with an instructor in a class of no more than twenty-four students. These interactive seminars are intended to stimulate the students’ curiosity and provide an opportunity to get to know a member of the professorial staff in a seminar environment during the first year of study. Details can be found at www.artsci.utoronto.ca/current/course/fyh-1/.

Humans have solved problems for millennia on computing devices by representing data as diverse numbers, text, images, sound and genomes, and then transforming the data.  A gentle introduction to designing programs (recipes) for systematically solving problems that crop up in diverse domains such as science, literature and graphics.  Social and intellectual issues raised by computing.  Algorithms, hardware, software, operating systems, the limits of computation.

Note: you may not take this course concurrently with any Computer Science course, but you may take CSC108H1/CSC148H1 after CSC104H1.

 To help you select the programming course that is right for you, see www.cs.toronto.edu/dcs > Choose Programs & Courses > Undergraduate Courses > Choosing Your First Year Courses.

Programming in a language such as Python. Elementary data types, lists, maps.  Program structure: control flow, functions, classes, objects, methods. Algorithms and problem solving. Searching, sorting, and complexity. Unit testing. No prior programming experience required.

NOTE: You may not take this course concurrently with CSC120H1/CSC148H1, but you may take CSC148H1 after CSC108H1.

An introduction to computer science for students in other sciences, with an emphasis on gaining practical skills. Introduction to programming with examples and exercises appropriate to the sciences; web programming; software tools.  Topics from: database design, considerations in numerical calculation, using UNIX/LINUX systems. At the end of this course you will be able to develop computer tools for scientific applications, such as the structuring and analysis of experimental data. With some additional preparation, you will also be ready to go on to CSC148H1.  Practical (P) sections consist of supervised work in the computer laboratory. No programming experience is necessary.

Abstract data types and data structures for implementing them. Linked data structures. Encapsulation and information-hiding. Object-oriented programming. Specifications. Analyzing the efficiency of programs. Recursion. This course assumes programming experience as provided by CSC108H1. Students who already have this background may consult the Computer Science Undergraduate Office for advice about skipping CSC108H1. Practical (P) sections consist of supervised work in the computing laboratory. These sections are offered when facilities are available, and attendance is required. NOTE: Students may go to their college to drop down from CSC148H1 to CSC108H1. See above for the drop down deadline.

Introduction to abstraction and rigour. Informal introduction to logical notation and reasoning. Understanding, using and developing precise expressions of mathematical ideas, including definitions and theorems. Structuring proofs to improve presentation and comprehension. General problem-solving techniques. Running time analysis of iterative programs.  Formal definition of Big-Oh.  Diagonalization, the Halting Problem, and some reductions. Unified approaches to programming and theoretical problems.

The course will provide an informal, but rigorous treatment of a variety of topics, introducing students to the relevant background in graph theory, social network formation, incentives and game theory, and providing exposure to the relevant mathematical and computational tools required to analyze relevant phenomenon. Topics may include: structural analysis of social networks, matching markets, trading networks, web search, information cascades, prediction markets, and online advertising, among others.

Distribution Requirement Status: This is a Science course
Breadth Requirement: Society and its Institutions (3) + The Physical and Mathematical Universes (5)

CSC204H1    Collaborating with Computer Scientists[48L/24P]

This course teaches the language, culture, and communication mechanisms necessary for effective collaboration on large-scale software projects involving both computer scientists and non-computer scientists. This course is intended for students with little or no computer science background.

CSC207H1    Software Design[24L/12T]

An introduction to software design and development concepts, methods, and tools using a statically-typed object-oriented programming language such as Java. Topics from: version control, unit testing, refactoring, object-oriented design and development, design patterns, advanced IDE usage, regular expressions, and reflection.  Representation of floating-point numbers and introduction to numerical computation.

Software techniques in a Unix-style environment, using scripting languages and a machine-oriented programming language (typically C). What goes on in the operating system when programs are executed. Core topics: creating and using software tools, pipes and filters, file processing, shell programming, processes, system calls, signals, basic network programming.

The application of logic and proof techniques to Computer Science. Mathematical induction; correctness proofs for iterative and recursive algorithms; recurrence equations and their solutions (including the Master Theorem); introduction to automata and formal languages.  This course assumes university-level experience with proof techniques and algorithmic complexity as provided by CSC165H1.  Very strong students who already have this experience (e.g. successful completion of MAT157Y1) may consult the undergraduate office about proceeeding directly into CSC236H1.

The rigorous application of logic and proof techniques to Computer Science. Propositional and predicate logic; mathematical induction and other basic proof techniques; correctness proofs for iterative and recursive algorithms; recurrence equations and their solutions (including the Master Theorem); introduction to automata and formal languages. This course covers the same topics as CSC236H1, together with selected material from CSC165H1, but at a faster pace, in greater depth and with more rigour, and with more challenging assignments. Greater emphasis will be placed on proofs and theoretical analysis. Certain topics briefly mentioned in CSC165H1 or CSC236H1 may be covered in more detail in this course, and some additional topics may also be covered.

 NOTE: Students may go to their college to drop down from CSC240H1 to CSC165H1 (or to CSC236H1 if they have already passed CSC165H1). See above for the drop down deadline.

Computer structures, machine languages, instruction execution, addressing techniques, and digital representation of data. Computer system organization, memory storage devices, and microprogramming. Block diagram circuit realizations of memory, control and arithmetic functions. There are a number of laboratory periods in which students conduct experiments with digital logic circuits. 

Algorithm analysis: worst-case, average-case, and amortized complexity. Expected worst-case complexity, randomized quicksort and selection.  Standard abstract data types, such as graphs, dictionaries, priority queues, and disjoint sets. A variety of data structures for implementing these abstract data types, such as balanced search trees, hashing, heaps, and disjoint forests. Design and comparison of data structures. Introduction to lower bounds.

This course covers the same topics as CSC263H1, but at a faster pace, in greater depth and with more rigour, and with more challenging assignments. Greater emphasis will be placed on proofs, theoretical analysis, and creative problem-solving. Certain topics briefly mentioned in CSC263H1 may be covered in more detail in this course, and some additional topics may also be covered. Students without the exact course prerequisites but with a strong mathematical background are encouraged to consult the Department about the possibility of taking this course.

 NOTE: Students may go to their college to drop down from CSC265H1 to CSC263H1. See above for the drop down deadline.

If you are not in our Major or Specialist program, you are limited to three 300-/400-level CSC/ECE half-courses.

Privacy and Freedom of Information; recent Canadian legislation and reports. Computers and work; employment levels, quality of working life. Electronic fund transfer systems; transborder data flows. Computers and bureaucratization. Computers in the home; public awareness about computers. Robotics. Professionalism and the ethics of computers. The course is designed not only for science students, but also those in social sciences or humanities.

An introduction to agile development methods appropriate for medium-sized teams and rapidly-moving projects. Basic software development infrastructure; requirements elicitation and tracking; estimation and prioritization; teamwork skills; basic UML; design patterns and refactoring; security, discussion of ethical issues, and professional responsibility.

An introduction to the theory and practice of large-scale software system design, development, and deployment. Project management; advanced UML; reverse engineering; requirements inspection; verification and validation; software architecture; performance modeling and analysis.

An introduction to software development on the web. Concepts underlying the development of programs that operate on the web; survey of technological alternatives; greater depth on some technologies. Operational concepts of the internet and the web, static client content, dynamic client content, dynamically served content, n-tiered architectures, web development processes, and security on the web. Assignments involve increasingly more complex web-based programs. Guest lecturers from leading e-commerce firms will describe the architecture and operation of their web sites.

Measuring information. The source coding theorem. Data compression using ad hoc methods and dictionary-based methods. Probabilistic source models, and their use via Huffman and arithmetic coding. Noisy channels and the channel coding theorem. Error correcting codes, and their decoding by algebraic and probabilistic methods.

User-centred design of interactive systems; methodologies, principles, and metaphors; task analysis. Interdisciplinary design; the role of graphic design, industrial design, and the behavioural sciences. Interactive hardware and software; concepts from computer graphics. Typography, layout, colour, sound, video, gesture, and usability enhancements. Classes of interactive graphical media; direct manipulation systems, extensible systems, rapid prototyping tools. Students work on projects in interdisciplinary teams. Enrolment limited, but non-computer scientists welcome.

Image synthesis and image analysis aimed at students with an interest in computer graphics, computer vision, or the visual arts. Focus on three major topics: (1) visual computing principles—computational and mathematical methods for creating, capturing, analyzing, and manipulating digital photographs (image acquisition, basic image processing, image warping, anti-aliasing); (2) digital special effects—applying these principles to create special effects found in movies and commercials; (3) visual programming—using C/C++ and OpenGL to create graphical user interfaces for synthesizing and manipulating photographs. The course requires the ability to use differential calculus in several variables and linear algebra.

The first half of the course is about supervised learning for regression and classification problems and will include the perceptron learning procedure, backpropagation, and methods for ensuring good generalisation to new data. The second half of the course is about unsupervised learning methods that discover hidden causes and will include K-means, the EM algorithm, Boltzmann machines, and deep belief nets.

Programming principles common in modern languages; details of commonly used paradigms. The structure and meaning of code. Scope, control flow, datatypes and parameter passing. Two non-procedural, non-object-oriented programming paradigms: functional programming (illustrated by languages such as Lisp/Scheme, ML or Haskell) and logic programming (typically illustrated in Prolog).

The study of computational methods for solving problems in linear algebra, non-linear equations, and approximation. The aim is to give students a basic understanding of both floating-point arithmetic and the implementation of algorithms used to solve numerical problems, as well as a familiarity with current numerical computing environments.

Introduction to database management systems. The relational data model. Relational algebra. Querying and updating databases: the query language SQL. Application programming with SQL. Integrity constraints, normal forms, and database design. Elements of database system technology: query processing, transaction management.

Introduction to computer networks with an emphasis on fundamental principles. Basic understanding of computer networks and network protocols. Topics include network hardware and software, routing, addressing, congestion control, reliable data transfer, performance analysis, local area networks, and TCP/IP.

Principles of operating systems. The operating system as a control program and as a resource allocator. The concept of a process and concurrency problems: synchronization, mutual exclusion, deadlock. Additional topics include memory management, file systems, process scheduling, threads, and protection.

Development of embedded software for control and monitoring. Techniques for efficient running of multiple real-time, critical multiple processes and for device control. Methods of working on small systems, such as microcontroller-based systems. Projects use microprocessors to control equipment with feedback from sensors. Design, implementation, and testing of software using a language such as C.  Ordinarily offered in years alternating with ECE385H1.

Standard algorithm design techniques: divide-and-conquer, greedy strategies, dynamic programming, linear programming, randomization, network flows, approximation algorithms.  Brief introduction to NP-completeness: polynomial time reductions, examples of various NP-complete problems, self-reducibility. Students will be expected to show good design principles and adequate skills at reasoning about the correctness and complexity of algorithms.

Theories and algorithms that capture (or approximate) some of the core elements of computational intelligence. Topics include: search; logical representations and reasoning, classical automated planning, representing and reasoning with uncertainty, learning, decision making (planning) under uncertainty. Assignments provide practical experience, both theory and programming, of the core topics.

A hardware-oriented course dealing with microprocessor and embedded systems. Microprocessor structures, memory and cache structures, input/output techniques, peripheral device control, hardware system and programming considerations. Laboratory experiments provide "hands-on" experience.  Ordinarily offered in years alternating with CSC372H1.

If you are not in our Major or Specialist program, you are limited to three 300-/400-level CSC/ECE half-courses.

Introduction to techniques involving natural language and speech in applications such as information retrieval, extraction, and filtering; intelligent Web searching; spelling and grammar checking; speech recognition and synthesis; and multi-lingual systems including machine translation. N-grams, POS-tagging, semantic distance metrics, indexing, on-line lexicons and thesauri, markup languages, collections of on-line documents, corpus analysis. PERL and other software.

Concepts and techniques for the design and development of electronic games. History, social issues and story elements. The business of game development and game promotion. Software engineering, artificial intelligence, and graphics elements. Level and model design. Audio elements. Practical assignments leading to team implementation of a complete game.

Concepts and state of the art techniques in quality assessment for software engineering; quality attributes; formal specifications and their analysis; testing, verification, and validation.

An introduction to methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods, decision trees, linear models, and neural networks. Clustering algorithms. Problems of overfitting and of assessing accuracy. Problems with handling large databases. Emphasis on statistical approaches.

An introduction to probability as a means of representing and reasoning with uncertain knowledge. Qualitative and quantitative specification of probability distributions using probabilistic graphical models. Algorithms for inference and probabilistic reasoning with graphical models. Statistical approaches and algorithms for learning probability models from empirical data. Applications of these models in artificial intelligence and machine learning.

Identification and characterization of the objects manipulated in computer graphics, the operations possible on these objects, efficient algorithms to perform these operations, and interfaces to transform one type of object to another. Display devices, display data structures and procedures, graphical input, object modelling, transformations, illumination models, primary and secondary light effects; graphics packages and systems. Students, individually or in teams, implement graphical algorithms or entire graphics systems.

Introduction to basic concepts in computer vision. Extraction of image features at multiple scales. Robust estimation of model parameters. Multiview geometry and reconstruction. Image motion estimation and tracking. Object recognition. Topics in scene understanding as time permits.

Understanding human behaviour as it applies to user interfaces: work activity analysis, observational techniques, questionnaire administration, and unobtrusive measures. Operating parameters of the human cognitive system, task analysis and cognitive modelling techniques and their application to designing interfaces. Interface representations and prototyping tools. Cognitive walkthroughs, usability studies and verbal protocol analysis. Case studies of specific user interfaces.

Numerical algorithms for the algebraic eigenvalue problem, approximation, integration, and the solution of ordinary differential equations. Emphasis is on the convergence, stability and efficiency properties of the algorithms. 

Computable functions, Church's thesis, unsolvable problems, recursively enumerable sets. Predicate calculus, including the completeness, compactness, and Lowenheim-Skolem theorems. Formal theories and the Gödel Incompleteness Theorem. Ordinarily offered in years alternating with CSC448H1.

Implementation of database management systems. Storage management, indexing, query processing, concurrency control, transaction management. Database systems on parallel and distributed architectures. Modern database applications: data mining, data warehousing, OLAP, data on the web. Object-oriented and object-relational databases.

Finite difference methods for hyperbolic and parabolic equations; consistency, convergence, and stability. Finite element methods for 2-point boundary value problems and elliptic equations. Special problems of interest. Ordinarily offered in years alternating with CSC456H1.

Regular, deterministic, context free, context sensitive, and recursively enumerable languages via generative grammars and corresponding automata (finite state machines, push down machines, and Turing machines). Topics include complexity bounds for recognition, language decision problems and operations on languages.  Ordinarily offered in years alternating with CSC438H1.

The software and internet industries; principles of operation for successful software enterprises; innovation and entrepreneurship; software business definition and planning; business models, market and product planning; product development, marketing, sales, and support; financial management and financing of high-technology ventures; management, leadership, and partnerships. Students will all write business plans in teams.

Computationally intensive applications in science and engineering are implemented on the fastest computers available, today composed of many processors operating in parallel. Parallel computer architectures; implementation of numerical algorithms on parallel architectures; performance evaluation. Topics from: matrix-vector product, solution of linear systems, sparse matrices, iterative methods, domain decomposition, Fourier solvers. For students in computer science, applied mathematics, science, engineering. Ordinarily offered in years alternating with CSC446H1.

Computer networks with an emphasis on network programming and applications. An overview of networking basics: layering, naming, and addressing, packet switching fundamentals, socket programming, protocols, congestion control, routing, network security, wireless networks, multimedia, web 2.0, software-defined networking, and online social networks.

Introduction to the theory of computability: Turing machines and other models of computation, Church’s thesis, computable and noncomputable functions, recursive and recursively enumerable sets, many-one reductions. Introduction to complexity theory: P, NP, polynomial time reducibility, NP-completeness, self-reducibility, space complexity (L, NL, PSPACE and completeness for those classes), hierarchy theorems, and provably intractable problems.

Using mathematics to write error-free programs. Proving each refinement; identifying errors as they are made. Program development to meet specifications; modifications that preserve correctness. Useful for all programming; essential for programs that lives depend on. Basic logic, formal specifications, refinement. Conditional, sequential, parallel, interaction, probabilistic programming, and functional programming.

Numerical methods for unconstrained optimization problems, in particular line search methods and trust region methods. Topics include steepest descent, Newton's method, quasi-Newton methods, conjugate gradient methods and techniques for large problems. This course will normally be offered every other year.

An in-depth exploration of the major components of operating systems with an emphasis on the techniques, algorithms, and structures used to implement these components in modern systems. Project-based study of process management, scheduling, memory management, file systems, and networking is used to build insight into the intricacies of a large concurrent system.

Computational linguistics and the processing of language by computer. Topics include: context-free grammars; chart parsing, statistical parsing; semantics and semantic interpretation; ambiguity resolution techniques; reference resolution. Emphasis on statistical learning methods for lexical, syntactic, and semantic knowledge.

Representing knowledge symbolically in a form suitable for automated reasoning, and associated reasoning methods. Topics from: first-order logic, entailment, the resolution method, Horn clauses, procedural representations, production systems, description logics, inheritance networks, defaults and probabilities, tractable reasoning, abductive explanation, the representation of action, planning.

The structure of compilers, Programming language processing. Scanning based on regular expressions, Parsing using context free grammars, Semantic analysis (type and usage checking), Compiler dictionaries and tables. Runtime organization and storage allocation, code generation, optimization. Use of modern compiler building tools. Course project involves building a complete compiler.

Theoretical and practical aspects of building modern optimizing compilers. Topics: intermediate representations, basic blocks and flow graphs, data flow analysis, partial evaluation and redundancy elimination, loop optimizations, register allocation, instruction scheduling, interprocedural analysis, and memory hierarchy optimizations. Students implement significant optimizations within the framework of a modern research compiler. (This course is a cross-listing of ECE540H1, Faculty of Applied Science and Engineering.)

This half-course gives students experience solving a substantial problem that may span several areas of Computer Science. Students will define the scope of the problem, develop a solution plan, produce a working implementation, and present their work using written, oral, and (if suitable) video reports. Class time will focus on the project, but may include some lectures. The class will be small and highly interactive. Project themes change each year. Contact the Computer Science Undergraduate Office for information about this year’s topic themes,  required preparation and course enrolment procedures. Not eligible for CR/NCR option.

This half-course gives students experience solving a substantial problem that may span several areas of Computer Science. Students will define the scope of the problem, develop a solution plan, produce a working implementation, and present their work using written, oral, and (if suitable) video reports. Class time will focus on the project, but may include some lectures. The class will be small and highly interactive. Project themes change each year. Contact the Computer Science Undergraduate Office for information about this year’s topic themes,  required preparation and course enrolment procedures. Not eligible for CR/NCR option.

This half-course involves a significant project in any area of Computer Science. The project may be undertaken individually or in small groups. The course is offered by arrangement with a Computer Science faculty member. Not eligible for CR/NCR option.

This half-course involves a significant project in any area of Computer Science. The project may be undertaken individually or in small groups. The course is offered by arrangement with a Computer Science faculty member. Not eligible for CR/NCR option.