# Csc209 Assignment 1

Faculty of Arts & Science 2016-2017 Calendar |
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## Computer Science

## Faculty

University Professor Emeritus

S. Cook, SM, PhD, FRS, FRSC

G. Hinton, PhD, FRS, FRSC

Professors Emeriti

R. Baecker, MSc, PhD

D. Corneil, MA, PhD

W. Enright, MSc, PhD (University of Toronto Scarborough)

C. Gotlieb, MA, PhD, DMath, DEng, FRSC

E. Hehner, MSc, PhD

R.C. Holt, PhD

H. Levesque, MSc, PhD, FRSC

R. Mathon, MSc, PhD (University of Toronto Mississauga)

J. Mylopoulos, MSc, PhD, FRSC

C. Rackoff, PhD (University of Toronto Mississauga)

D. Wortman, MSc, PhD

Senior Lecturer Emeritus

J. Clarke, MSc, PhD

University Professor

A. Borodin, MSc, PhD, FRSC

Professor and Chair of the Department

R. Balakrishnan, MSc, PhD

Professor and Vice Chair of the Department

M. Chechik, MSc, PhD

Professor and Associate Chair (Graduate Studies)

P. Marbach, MSc, PhD

Associate Professor, Teaching Stream and Associate Chair (Undergraduate Studies)

F. Pitt, MSc, PhD

Professors

T. Abdelrahman, MSc, PhD

F. Bacchus, MSc, PhD

R. Balakrishnan, MSc, PhD

C. Boutilier, MSc, PhD

M. Chechik, MSc, PhD

E. de Lara, MSc, PhD

S. Dickinson, MSc, PhD

S. Easterbrook, PhD

F. Ellen, MMath, PhD

E. Fiume, MSc, PhD

D. Fleet, MSc, PhD (University of Toronto Scarborough)

V. Hadzilacos, PhD (University of Toronto Scarborough)

G. Hirst, MSc, PhD (University of Toronto Scarborough)

K. Jackson, MSc, PhD

A. Jepson, PhD

N. Koudas, MSc, PhD (University of Toronto Scarborough)

K. Kutulakos, MSc, PhD

P. Marbach, MSc, PhD

S. McIlraith, MMath, PhD

R. Miller, MSc, PhD, FRSC

M. Molloy, MMath, PhD (University of Toronto Scarborough)

R. Neal, BSc, PhD

G. Penn, MSc, PhD

T. Pitassi, MSc, PhD

K. Singh, MSc, PhD

S. Stevenson, MSc, PhD

S. Toueg, MA, PhD

R. Zemel, MSc, PhD

Associate Professors

A. Bonner, MSc, PhD (University of Toronto Mississauga)

M. Brudno, MSc, PhD

C. Christara, MSc, PhD

J. Danahy, MScUrb & DesPl

A. Demke-Brown, MSc, PhD

A. Farzan, PhD

Y. Ganjali, MSc, PhD

B. Schroeder, MSc, PhD (University of Toronto Scarborough)

K. Truong, PhD

Assistant Professors

S. Fidler, PhD

A. Nikolov, PhD

B. Rossman, PhD

R. Salakhutdinov, PhD

R. Urtasun, PhD

D. Wigdor, MSc, PhD (University of Toronto Mississauga)

Associate Professors, Teaching Stream

G. Baumgartner, MSc

J. Campbell, MMath

M. Craig, MSc

S. Engels, MMath

T. Fairgrieve, MSc, PhD

P. Gries, MEng

D. Heap, MSc

D. Horton, MSc

F. Pitt, MSc, PhD

K. Reid, MSc

Assistant Professors, Teaching Stream

D.Liu, MSc

J.Smith, MSc

Cross Appointed

C. Amza, PhD

G. Bader, PhD

C. Beck, PhD

M. Consens, PhD

B. Frey, PhD

A. Goel, PhD

M. Gruninger, PhD

A. Jacobsen, MSc, PhD

P. Kim, PhD

B. Li, MSc, PhD

D. Lie, PhD

J. Liebeherr, PhD

K. Lyons, MSc, PhD

E. Mendelsohn, MSc, PhD (Professor Emeritus) (University of Toronto Scarborough)

A. Mihailidis, PhD

Q. Morris, PhD

A. Moses, PhD

C. Munteanu, PhD

F. Roth, PhD

D. Roy, PhD

M. Stumm, MSc (Math), PhD

A. Urquhart, MA, PhD (Professor Emeritus)

A. Veneris, MSc, PhD

E. Yu, MSc, PhD

D. Yuan, PhD

Z. Zhang, PhD

M. Chignell, MSc, PhD

Adjunct and Status Only

P. Andritsos, PhD

D. Aruliah, PhD

J. Birnholtz, PhD

A. Borgida, PhD

B. Buxton, MSc

F. Chevalier, PhD

A. Fazley, PhD

C. Forlines, PhD

A. Goldenberg, PhD

M. Grech, MBA

B. Haibe-Kains, PhD

A. Hertzmann, PhD

M. Hoffman, PhD

R. Johnson, PhD

I. Jurisica, PhD

G. Lakemeyer, PhD

C. Landreth, MS

Y. Lesperance, MSc, PhD

R. Lilien, PhD

K. Moffat, PhD

J. Parkinson, PhD

K. Pu, PhD

F. Rudzicz, PhD

P. Salvini, PhD

R. Schmidt, PhD

J. Simpson, PhD

J. Stam, PhD

B. Taati, PhD

T. Topalouglou, PhD

J. Tsotsos, PhD

Introduction

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 the Student Services & Resources chapter of this Calendar.

Associate Chair (Undergraduate Studies): Associate Professor, Teaching Stream 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.

Computer Science Specialist (Science program)This is a limited enrolment program (Type 2L) that can only accommodate a certain number of students. Eligibility is based on the following criteria:

A. Completion of at least 4.0 FCEs including CSC148H1/CSC207H1 (with a minimum grade of 60%) and CSC165H1/CSC236H1/CSC240H1 (with a minimum grade of 60%), AND

B. An average of the grades in CSC148H1/CSC207H1 and CSC165H1/CSC236H1/CSC240H1 that meets the department's annual cutoff. When more than one course has been completed from a list of alternatives, the higher grades will be used. Also, CSC240H1 grades will be adjusted to account for the course's greater difficulty. Finally, note that *the cutoff changes from year to year,* depending on the current capacity of the program and the pool of applicants. For more information, including historical data, please visit http://web.cs.toronto.edu/program/ugrad/admission.htm.

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 for program completion; 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 for program completion; 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:

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.

Computer Science Specialist: Focuses

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, or 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 program enrolment web site. Focuses can be chosen on ACORN 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, Machine Learning, and Knowledge Representation and Reasoning. 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 (as in point 2, below).

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, so that courses are from at least two of the four areas

a) CSC401H1, CSC485H1

b) CSC320H1, CSC420H1

c) CSC321H1, CSC411H1, CSC412H1

d) CSC384H1, CSC486H1

Suggested Related Courses:

CSC304H1, 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 automatically process 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: CSC412H1, CSC418H1, 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 modelling), 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. IRE260H1**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, CSC473H1, 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, 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 (Type 2L) that can only accommodate a certain number of students. Eligibility is based on the following criteria:

A. Completion of at least 4.0 FCEs including CSC148H1/CSC207H1 (with a minimum grade of 60%) and CSC165H1/CSC236H1/CSC240H1 (with a minimum grade of 60%), AND

B. An average of the grades in CSC148H1/CSC207H1 and CSC165H1/CSC236H1/CSC240H1 that meets the department's annual cutoff. When more than one course has been completed from a list of alternatives, the higher grades will be used. Also, CSC240H1 grades will be adjusted to account for the course's greater difficulty. Finally, note that the cutoff changes from year to year, depending on the current capacity of the program and the pool of applicants. For more information, including historical data, please visit http://web.cs.toronto.edu/program/ugrad/admission.htm.

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 for program completion; 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 for program completion; 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:

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.

Advice on choosing courses towards a Major in Computer Science

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 also 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. CSC304H1 (Algorithmic Game Theory and Mechanism Design) and CSC454H1 (Business of Software) would also complement 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.

Computer Science Minor (Science program)

This is a limited enrolment program (Type 2L) that can only accommodate a certain number of students. Eligibility is based on the following criteria:

A. Completion of at least 4.0 FCEs including CSC148H1/CSC207H1 (with a minimum grade of 60%) and CSC165H1/CSC236H1/CSC240H1 (with a minimum grade of 60%), AND

B. An average of the grades in CSC148H1/CSC207H1 and CSC165H1/CSC236H1/CSC240H1 that meets the department's annual cutoff. When more than one course has been completed from a list of alternatives, the higher grades will be used. Also, CSC240H1 grades will be adjusted to account for the course's greater difficulty. Finally, note that the cutoff changes from year to year, depending on the current capacity of the program and the pool of applicants. For more information, including historical data, please visit http://web.cs.toronto.edu/program/ugrad/admission.htm.

(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

Enrolment notes

1. The University of Toronto Mississauga and University of Toronto Scarborough Computer Science Minor subject POSts are not recognized as restricted Computer Science programs 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

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.

Dropping down from enriched to regular courses

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**:

20169, Fall session: October 7, 2016

20171, Winter session: February 1, 2017

Students with transfer credits

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.

100-level courses

First Year Seminars

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/.

CSC104H1 Computational Thinking[24L/12T]

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.

Exclusion: Any Computer Science courseDistribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

Choosing first year courses

To help you select the programming course that is right for you, see http://web.cs.toronto.edu/program/ugrad/courses_ug/1st.htm

CSC108H1 Introduction to Computer Programming[36L]

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.

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC120H1 Computer Science for the Sciences[24L/12P]

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.

Exclusion: Any CSC course, with the exception of CSC104H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC121H1 Computer Science for Statistics[24L/12P]

An introduction to computer science for students planning to use computers for statistical analysis and research. Using a statistical programming environment, fundamental programming concepts, and computational topics relevant to statistics, such as issues with numerical calculation, random number generation, and management of data. Practicals consist of supervised work in the computer laboratory to reinforce concepts and develop programming skills. No previous programming experience is necessary. Please consult with the CS Undergraduate office if you intend to continue on to CSC148H1.

Exclusion: Any CSC course, with the exception of CSC104H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC148H1 Introduction to Computer Science[36L/24P]

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.

Prerequisite: CSC108H1/(equivalent programming experience)Exclusion: CSC150H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC165H1 Mathematical Expression and Reasoning for Computer Science[36L/24T]

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.

Corequisite: CSC148H1/(CSC108H1/CSC120H1, MAT137Y1/MAT157Y1)Exclusion: CSC236H1, CSC240H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

200-level courses

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.

Distribution Requirement Status: HumanitiesBreadth Requirement: The Physical and Mathematical Universes (5)

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.

Prerequisite: 60% or higher in CSC148H1/CSC150H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC209H1 Software Tools and Systems Programming[24L/12T]

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.

Prerequisite: CSC207H1Exclusion: CSC372H1, CSC369H1, CSC469H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC236H1 Introduction to the Theory of Computation[24L/12T]

The application of logic and proof techniques to Computer Science. Mathematical induction; correctness proofs for iterative and recursive algorithms; recurrence equations and their solutions; 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 proceeding directly into CSC236H1.

Prerequisite: 60% or higher in CSC148H1/CSC150H1, 60% or higher in CSC165H1Exclusion: CSC240H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC240H1 Enriched Introduction to the Theory of Computation[24L/12T]

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.

Corequisite: CSC148H1/CSC150H1; MAT137Y1/MAT157Y1Exclusion: CSC236H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC258H1 Computer Organization[24L/12T/13P]

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.

Prerequisite: 60% or higher in CSC148H1/CSC150H1, 60% or higher in CSC165H1/CSC240H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC263H1 Data Structures and Analysis[24L/12T]

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.

Prerequisite: CSC207H1, CSC236H1/CSC240H1; STA247H1/STA255H1/STA257H1Exclusion: CSC265H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC265H1 Enriched Data Structures and Analysis[24L/12T]

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.

Prerequisite: CSC240H1 or an A- in CSC236H1Corequisite: STA247H1/STA255H1/STA257H1

Exclusion: CSC263H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

300-level courses

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

CSC300H1 Computers and Society[24L/12T]

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.

Prerequisite: Any half-course on computingDistribution Requirement Status: Science

Breadth Requirement: Society and its Institutions (3)

CSC301H1 Introduction to Software Engineering[24L/12T]

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.

Prerequisite: CSC209H1, CSC263H1/CSC265H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC302H1 Engineering Large Software Systems[24L/12T]

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 modelling and analysis.

Prerequisite: CSC301H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC304H1 Algorithmic Game Theory and Mechanism Design[24L/12P]

A mathematical and computational introduction to game theory and mechanism design. Topics include games in matrix and extensive form, equilibria and price of anarchy, matching markets, auctions, network externalities, tipping points, voting theory. This course is intended for economics, mathematics, and computer science students.

Prerequisite: STA247H1/STA255H1/STA257H1/PSY201H1/ECO227Y1, (MAT135H1, MAT136H1)/MAT137Y1/MAT157Y1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC309H1 Programming on the Web[24L/12T]

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.

Prerequisite: CSC209H1Recommended Preparation: CSC343H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC310H1 Information Theory[24L/12T]

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.

Prerequisite: 60% or higher in CSC148H1/CSC150H1; STA247H1/STA255H1/STA257H1/STA107H1; (MAT135H1, MAT136H1)/ MAT135Y1/MAT137Y1/MAT157Y1, MAT221H1/MAT223H1/MAT240H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC318H1 The Design of Interactive Computational Media[24L/12T]

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.

Prerequisite: Any CSC half-courseRecommended Preparation: CSC300H1 provides useful background for work in CSC318H1, so if you plan to take CSC300H1 then you should do it before CSC318H1

Distribution Requirement Status: Science

Breadth Requirement: None

CSC320H1 Introduction to Visual Computing[24L/12P]

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.

Prerequisite: CSC209H1/(CSC207H1, proficiency in C or C++); MAT221H1/MAT223H1/MAT240H1, (MAT136H1 with a minimum mark of 77)/(MAT137Y1 with a minimum mark of 73)/(MAT157Y1 with a minimum mark of 67)/MAT235Y1/MAT237Y1/MAT257Y1Recommended Preparation: MAT235Y1/MAT237Y1/MAT257Y1

Distribution Requirement Status: Science

Breadth Requirement: None

CSC321H1 Introduction to Neural Networks and Machine Learning[24L/12P]

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.

Prerequisite: (MAT136H1 with a minimum mark of 77)/(MAT137Y1 with a minimum mark of 73)/(MAT157Y1 with a minimum mark of 67)/MAT235Y1/MAT237Y1/MAT257Y1, MAT221H1/MAT223H1/MAT240H1; STA247H1/STA255H1/STA257H1Recommended Preparation: MAT235Y1/MAT237Y1/MAT257Y1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC324H1 Principles of Programming Languages[24L/12T]

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).

Prerequisite: CSC263H1/CSC265H1Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC336H1 Numerical Methods[24L/12T]

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.

Prerequisite: CSC148H1/CSC150H1; MAT133Y1(70%)/(MAT135H1, MAT136H1)/MAT135Y1/MAT137Y1/MAT157Y1, MAT221H1/MAT223H1/MAT240H1Exclusion: CSC350H1, CSC351H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC343H1 Introduction to Databases[24L/12T]

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.

Prerequisite: CSC165H1/CSC240H1/(MAT135H1, MAT136H1)/MAT135Y1/MAT137Y1/MAT157Y1; CSC207H1. Prerequisite for Engineering students only: ECE345H1/CSC190H1/CSC192H1Exclusion: CSC434H1

Distribution Requirement Status: Science

Breadth Requirement: The Physical and Mathematical Universes (5)

CSC358H1 Principles of Computer Networks[24L/12T]

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.

Prerequisite: CSC209H1, CSC258H1, CSC263H1/CSC265H1, STA247H1/STA255H1/STA257H1/ECO227Y1Distribution Requirement Status: Science

## Working Environment

You will be using the Git version control software to submit your assignments. Many of you will have learned to use Git in CSC207. More specific instructions will be provided once the repositories have been set up.

It is very important that you are confident that you can correctly submit your work by the deadline. One big advantage of using a version control system to submit your work is that you can commit and push your work early and often. This will ensure that you are not struggling with Git at the last minute.

As your assignments are submitted electronically and will often be tested using an automated testing program, you must follow the submission instructions exactly. Any program that does not compile on a CS Teaching lab machine will receive a grade of 0. If you can explain clearly in a remarking request how to fix the problem, your program will be remarked with a 20% penalty. Check your submission carefully; verify that you have submitted exactly the files you intended to submit and that they compile on a lab machine.

## Policies

For late penalty and remarking information, see the course syllabus.

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