A course learning outcome (CLO) is a brief statement of a skill, competency, or attitude a successful student will achieve by the end of a course; there are higher-level more abstract learning outcomes for entire programs.
This is the current version of the course learning outcomes catalog for the School of Computing as of April, 2024. Some were revised during the QUQAP Cyclical Review and Self-Study in 2022-23. Course websites may list additional learning outcomes for the current specific offering of the course.
Courses
CISC 101: Introduction to Computer Programming
Introduction to algorithms: their definition, design, coding, and execution on computers. Intended for students who have no programming experience.
| CLO | Supports PLO(s) |
|---|---|
| Design a computational solution to a problem described in natural language, express the solution in an algorithmic way, and convert the algorithm effectively and intelligibly into a procedural program. | QSC 3.1, QSC 3.2, QSC 3.3 |
| Ensure that algorithms produce the correct output and handle edge cases. | QSC 3.3 |
| Properly use modularity, repetition (both counted and unbounded), conditionals, and simple (rectangular) data structures in problem solutions | QSC 3.2, QSC 3.3 |
CISC 102: Discrete Structures I
Introduction to mathematical discourse and proof methods with a focus on discrete structures. Sets, sequences, and relations. Properties of the integers. Induction. Counting with permutations and combinations, pigeonhole principle. Principle of Inclusion-exclusion. Introduction to graphs and graph terminology.
| CLO | Supports PLO(s) |
|---|---|
| Analyze and/or solve problems using discrete structures. | QSC 2.1, QSC 2.2 |
| Construct mathematical proofs using basic proof methods. | QSC 2.2, QSC 4.2 |
| Apply graph theory to solve basic problems. | QSC 1.1, QSC 2.2 |
| Use concepts for discrete structures to produce correct computer code to solve problems. | QSC 1.1 |
| Communicate concepts and/or solutions using discrete structures to a technical audience. | QSC 4.1, QSC 4.2 |
CISC 110: Creative Computing
Introduction to fundamental programming concepts in the context of visual, interactive media. Students may develop applications in any domain (e.g., fine art, education, commerce, physical or social sciences) while learning about algorithms, program design, logic, program control flow, functions, testing, etc.
| CLO | Supports PLO(s) |
|---|---|
| Design a computational solution to a problem described in natural language, express the solution in an algorithmic way, and convert the algorithm effectively and intelligibly into a procedural program. | QSC 3.1, QSC 3.2 |
| Properly use modularity, repetition (both counted and unbounded), conditionals, and simple (rectangular) data structures in problem solutions | QSC 3.2, QSC 3.3 |
CISC 121: Introduction to Computing Science I
Introduction to design, analysis, and implementation of algorithms. Recursion, backtracking, and exits. Linear data structures (stacks and queues). Elementary searching and sorting. Order-of-magnitude complexity. Documentation, iterative program development, translating natural language to code, testing and debugging.
| CLO | Supports PLO(s) |
|---|---|
| Given a natural language problem description, develop (design, construct, debug and test) simple software in a conventional procedural programming language using an interactive IDE. | QSC 3.3 |
| Implement the basic searching and sorting algorithms: linear and binary search; bubble, insertion and/or selection, merge, and quick sort.Examine the design and use of computing-related concepts in specialized domains with respect to the disciplines’ paradigms. | QSC 1.1, QSC 2.1 |
| Analyse the worst-case computational complexity of small iterative and recursive programs | QSC 5.1 |
CISC 124: Introduction to Computing Science II
Introduction to software design and development with the object-oriented paradigm, and its effect on abstraction and component re-use. Working in groups using incremental development and version management. Test driven development. Numerical computation.
| CLO | Supports PLO(s) |
|---|---|
| Design and construct object oriented programs of 500 lines or less, including graphical user interfaces. | QSC 3.2, QSC 3.3 |
| Apply the programming practices of good style, documentation, testing, encapsulation, inheritance. | QSC 3.2, QSC 3.3 |
| Show the consequences of numeric representation on computers. | QSC 1.2, QSC 2.2 |
CISC 151: Elements of Computing with Data Analytics
Introduction to algorithms: their definition, design, coding, and execution on computers, with applications drawn from data analytics, including simple prediction and clustering. Intended for students who have no programming experience. All or most assignment work will be completed during lab time.
| CLO | Supports PLO(s) |
|---|---|
| Design a computational solution to a problem described in natural language, express the solution in an algorithmic way, and convert the algorithm effectively and intelligibly into a procedural program | QSC 2.1, QSC 3.1 |
| Properly use modularity, repetition (both counted and unbounded), conditionals, and simple (rectangular) data structures in problem solutions | QSC 3.3 |
| Plan ways to build simple models from data and interpret the meaning of the results | QSC 3.1, QSC 3.2, QSC 3.3 |
CISC 171: Computational Probability and Statistics
Introduction to the theory of probability. Discrete and continuous random variables, expectation, and variance. Independent random variables, conditional probability, and Bayes Theorem. Discrete and continuous probability distribution functions, including joint distribution functions. One-variable and two-variable statistical measures and hypothesis testing. Applications of statistics in computing. This course may contain group work at the discretion of the instructor.
There are currently no learning outcomes defined for CISC 171.
CISC 181: Digital Societies
This introductory course provides a broad overview and ethical implications of technological topics and trends in the digital world such as the Internet of Things (IoT), Social Networks, Security and Privacy, Data Analytics, and Artificial Intelligence (AI). No programming experience is required.
| CLO | Supports PLO(s) |
|---|---|
| Identify, describe and discuss current and evolving technologies relevant to digital societies | QSC 1.1, QSC 4.1, QSC 4.2 |
| Specify how these technologies are or could be used for business and personal use | QSC 1.1, QSC 4.1, QSC 4.2 |
| State the impact of these technologies on society | QSC 4.1, QSC 4.2 |
| Critically analyse these technologies in terms of their legal, ethical and social implications | QSC 4.1, QSC 4.2, QSC 6.1 |
CISC 203: Discrete Structures II
Advanced proof methods for discrete structures from an object-oriented perspective. Combinatorics: advanced counting methods. Solving and applying recurrence relations. Graph theory including graph algorithms, structural induction, and proofs.
| CLO | Supports PLO(s) |
|---|---|
| Critique and construct moderately sophisticated mathematical arguments such as proof by contradiction, proof by induction, proof by minimal counterexample, counting arguments, and recognition of orderings | QSC 2.2 |
| Apply discrete mathematical tools and models such as graph theory, probability, group theory and modular arithmetic to problems such as modelling relational data and networks, scheduling and resource allocation, network design, predicting expected performance, motion planning, cryptography | QSC 1.1, QSC 2.2 |
| Apply basic discrete probability techniques to computational tasks | QSC 1.1, QSC 2.2 |
| Build a foundation for further learning by exposure to multiple computer languages, development tools, and/or methodologies. | QSC 6.2 |
CISC 204: Logic for Computing Science
Elements of mathematical logic with computing applications. Formal proof systems for propositional and predicate logic. Interpretations, validity, and satisfiability. Introduction to soundness, completeness and decidability.
| CLO | Supports PLO(s) |
|---|---|
| Represent logical statements and descriptions declaratively in a logical language | QSC 3.1 |
| Synthesize a rigorous human-understandable English description of a set of logical statements | QSC 4.2 |
| Construct a syntactic proof as a series of rigorous inference steps in a logical deduction | QSC 1.1, QSC 1.2 |
| Analyze the semantics of a set of logical statements in a logical framework | QSC 2.2 |
CISC 220: System-Level Programming
Basic concepts of Unix-like systems. Shells and scripting. System-level programming in the C language. Software development tools and techniques.
| CLO | Supports PLO(s) |
|---|---|
| Explain the distinctions among operating system kernel, system software, and applications. | QSC 1.2 |
| Explain the basics of a Unix-like file system and answer questions about the effects of hard and symbolic links | QSC 1.2 |
| Design and construct moderately complex scripts using a Unix-like shell | QSC 3.2, QSC 3.3 |
| Use a Unix or Unix-like command line system to create files and run programs, including the use of multiple processes, pipes and I/O redirection. | QSC 3.3 |
| Write short scripts (up to 30 lines) using a Unix-like shell, including the use of variables, conditionals, loops, arithmetic, pipes and command substitution | QSC 3.2, QSC 3.3 |
| Design and construct moderately complex software in a second procedural programming language | QSC 3.1, QSC 3.2, QSC 3.3 |
| Write small C programs (up to 100 lines) including the use of pointers, strings, structs and file I/O | QSC 3.1, QSC 3.2, QSC 3.3 |
| Demonstrate good memory management in C programming, avoiding both dangling pointers and memory leaks | QSC 3.3 |
| Make simple use of multiple processes in C programs, including sending and receiving signals | QSC 3.3 |
| Use "make" or a similar tool to build and maintain programs involving multiple modules and scripts | QSC 3.3 |
| Choose suitable software development tools for managing packages, editing text, etc. | QSC 3.3 |
| Build a foundation for further learning by exposure to multiple computer languages, development tools, and methodologies. | QSC 6.2 |
CISC 221: Computer Architecture
The descriptive levels of computer architecture. Instruction-set architectures. Assembly Language. Data representation. Support for operating-system management and high-level languages. Input/output and interrupts. Designing for performance. Digital Logic.
| CLO | Supports PLO(s) |
|---|---|
| Create, analyze and debug short C and assembly language programs using Unix (linux) tools | QSC 1.2, QSC 3.2 |
| Identify the equivalence between blocks of assembly language and a higher level procedural language | QSC 1.2 |
| Create and/or analyze program segments involving the representation, manipulation, and identification of the limitations of computer data. | QSC 3.2, QSC 5.1 |
| Create and/or analyze program segments focusing on the performance differences of alternative programming choices that are affected by a computer's architecture. | QSC 1.2, QSC 3.2, QSC 3.3, QSC 5.1 |
| Explain how a computer's hardware resources are managed by the Operating System. | QSC 1.2 |
| Discuss and resolve in a team environment, case studies and issues relating to computer architecture and make defensible programming choices to best satisfy given requirements. | QSC 4.1, QSC 6.1 |
| Demonstrate interpersonal and team interaction skills in the development of two team programming projects. | QSC 3.2, QSC 3.3, QSC 4.1, QSC 6.1 |
CISC 223: Software Specifications
Introduction to techniques for specifying the behaviour of software, with applications of these techniques to design, verification and construction of software. Logic-based techniques such as loop invariants and class invariants. Automata and grammar-based techniques, with applications to scanners, parsers, user-interface dialogs and embedded systems. Computability issues in software specifications.
| CLO | Supports PLO(s) |
|---|---|
| Design specifications for the functional behaviour of algorithmic code fragments | QSC 3.1 |
| Formally verify the correctness of simple code fragments involving conditional statements and loops using logic based techniques; | QSC 2.1, QSC 3.1 |
| Design regular expressions, state transition diagrams and context-free grammars for formal languages | QSC 2.2 |
| Appraise the limitations of regular expressions and context-free grammars and to demonstrate that a language is nonregular; | QSC 5.1 |
| Appraise the limitations of algorithmic computation and prove that the halting problem is unsolvable. | QSC 5.1 |
CISC 226: Game Design
An introduction to techniques for designing elementary computer games. Topics will include game development tools and processes, principles of game design, game prototyping and game evaluation.
| CLO | Supports PLO(s) |
|---|---|
| Apply a development process to design, evaluate and implement a digital game | QSC 1.1, QSC 3.2, QSC 3.3, QSC 4.1 |
| Apply creative techniques for stimulating the generation of game design ideas | QSC 1.1, QSC 4.1 |
| Perform early-stage evaluation of games using paper prototyping | QSC 1.1, QSC 4.1 |
| Discuss the elements contributing to the design of a game and use these elements to critique existing designs | QSC 4.1, QSC 4.2 |
| Discuss controversial aspects of game design such as game addiction and violence in games | QSC 4.1, QSC 6.1 |
CISC 235: Data Structures
Design and implementation of advanced data structures and related algorithms, including correctness and complexity analysis. Tradeoffs among alternative implementations of a module interface.
| CLO | Supports PLO(s) |
|---|---|
| Choose among alternative data structures to solve specific data-representation and algorithmic problems. | QSC 2.2, QSC 3.3 |
| Be familiar with common data structures, including: trees and graphs; stacks and queues; binary trees; AVL trees; Red-Black Trees; Heaps; Tries; Hash Tables; Bloom Filters; Skip Lists | QSC 2.1, QSC 2.2, QSC 5.1 |
| Be familiar with the fundamental algorithms for manipulating these structures | QSC 2.1, QSC 2.2, QSC 5.1 |
| Have practical experience implementing these structures | QSC 3.2, QSC 3.3 |
| Understand the use of complexity analysis to determine which data structure is most efficient and appropriate for use in a particular application | QSC 2.1, QSC 2.2, QSC 5.1 |
| Be able to determine the computational complexity for algorithms related to data structures (both upper and lower bounds) | QSC 5.1 |
CISC 251: Data Analytics
Introduction to data analytics; data preparation; assessing performance; prediction methods such as decision trees, random forests, support vector machines, neural networks and rules; ensemble methods such as bagging and boosting; clustering techniques such as expectation-maximization, matrix decompositions, and bi-clustering; attribute selection.
| CLO | Supports PLO(s) |
|---|---|
| Design inductive model building algorithms appropriate for datasets of moderate size and complexity | QSC 1.1, QSC 3.1, QSC 4.2 |
| Evaluate the modelling performance of such algorithms, and the implications for the real-world system that the data describes | QSC 3.2, QSC 3.3, QSC 6.1 |
CISC 271: Linear Methods for Artificial Intelligence
Elements of linear algebra for artificial intelligence, including: vector spaces; matrix decompositions; principal components analysis; linear regression; hyperplane classification of vectorial data; validation and cross-validation.
| CLO | Supports PLO(s) |
|---|---|
| Select and implement algorithms for vectorial data | QSC 1.1, QSC 3.3 |
| Synthesize data and solution methods for principal-component analysis | QSC 1.1 |
| Implement, test and evaluate methods for linear regression | QSC 2.1 |
| Interpret and explain methods and solutions in data classification | QSC 3.3, QSC 4.2 |
| Evaluate and critique performance of algorithms in data classification | QSC 5.1 |
CISC 282: Fundamentals of Web Development
This course surveys current best practices for implementing attractive, usable, secure and maintainable web applications. Other issues considered include: accessibility, platform and browser independence, licensing of intellectual property, scalability, user privacy, and using web technologies in mobile development.
| CLO | Supports PLO(s) |
|---|---|
| Write and make available to the public well formulated HTML pages with appropriate styling through CSS | QSC 1.1, QSC 3.2, QSC 6.1 |
| Identify when it is appropriate to use server-side programming such as PHP and to write simple PHP code to perform some functionality for a web application | QSC 1.2, QSC 3.2, QSC 3.3, QSC 6.1 |
| Identify when it is appropriate to use client-side programming such as JavaScript and write simple JavaScript code to make a web application interactive | QSC 1.1, QSC 3.2, QSC 3.3, QSC 6.1 |
CISC 320: Fundamentals of Software Development
Introduction to management of small and medium-scale software projects. Advanced programming methodology using the programming language C++. Includes a significant programming project.
| CLO | Supports PLO(s) |
|---|---|
| Design and construct medium-sized console and GUI programs in both procedural and object-oriented paradigms. | QSC 3.2, QSC 3.3 |
| Design and construct 500 to 1000 line console and GUI C++ programs using both Object Oriented and procedural C++ code. | QSC 3.2, QSC 3.3 |
| Contribute to the design and construction of a larger software project as a member of an agile programming team. | QSC 4.1, QSC 4.2, QSC 6.1 |
| Use software engineering techniques and tools to aid in the development of larger scale software projects. | QSC 3.3 |
CISC 322: Software Architecture
Abstractions and patterns of interactions and relationships among modules. Design recovery; relationship of architecture to requirements and testing.
| CLO | Supports PLO(s) |
|---|---|
| Recognize and describe the various architecture styles and design patterns | QSC 1.2 |
| Analyze the tradeoffs between competing software architecture styles and design patterns | QSC 3.2, QSC 5.1 |
| Recover, document, present, and discuss the architecture of a pre-existing software system using high level diagrams (such as UML and basic box and arrow diagrams). | QSC 1.1, QSC 1.2, QSC 4.1 |
| Specify, design and possibly implement a feature in a pre-existing code base. | QSC 3.1, QSC 3.2, QSC 3.3 |
| Work effectively in a group setting | QSC 6.1 |
CISC 324: Operating Systems
Layered operating systems for conventional shared memory computers: concurrent processes. Synchronization and communication. Concurrent algorithms. Scheduling. Deadlock. Memory management. Protection. File systems. Device management. Typical layers.
| CLO | Supports PLO(s) |
|---|---|
| Describe the representation of processes and threads, and the concepts of inter-process communication and synchronization. Assessed on programming assignments. | QSC 3.1, QSC 3.2 |
| Describe the steps in a context switch: process control blocks, the role of interrupts, the ready queue, CPU scheduling algorithms. Assessment is via short answer questions on assignments and exams. | QSC 1.2, QSC 2.1, QSC 3.1, QSC 4.2 |
| Design, code and analyse concurrent programs using semaphores or monitors, with awareness of pitfalls such as race conditions, deadlock and starvation. Assessment is via programming assignments and programming questions on exams. | QSC 3.3 |
| Understand and apply memory management concepts including address spaces, sharing and protection, paging, segmentation, translation lookaside buffer, and page replacement algorithms. Assessment is via short answer questions on assignments and exams. | QSC 1.2, QSC 2.1, QSC 3.1, QSC 4.2 |
| Describe organizational principles for operating systems: layers, kernel. Assessment is via short answer questions on assignments and exams. | QSC 1.2, QSC 4.2 |
| Explain how a computer's hardware resources are managed by the Operating System. | QSC 1.2, QSC 4.2 |
CISC 325: Human-Computer Interaction
Developing usable software requires that human factors be considered throughout the design and development process. This course introduces a series of techniques for development and evaluating usable software, and shows how these techniques can be integrated into a process for software development.
There are currently no learning outcomes defined for CISC 325.
CISC 326: Game Architecture
An introduction to software architectural design through the application domain of game development. Abstractions and patterns of interactions and relationships among modules. Design recovery. Relationship to requirements and testing
| CLO | Supports PLO(s) |
|---|---|
| Describe the various architectural styles and design patterns and analyze tradeoffs among them. | QSC 1.2, QSC 3.2, QSC 5.1 |
| Specify, design, and possibly implement a feature in a pre-existing code base. | QSC 3.1, QSC 3.2, QSC 3.3 |
| Work effectively in a group setting. | QSC 6.1 |
CISC 327: Software Quality Assurance
Validation of software throughout the life cycle. Comparative effectiveness in defect removal of formal methods (proofs of correctness), inspection (walkthroughs and reviews), and testing (unit, integration, and system testing; white box versus black box).
| CLO | Supports PLO(s) |
|---|---|
| Choose appropriate software process models | QSC 3.3 |
| Describe the inspection process, and the advantages of inspection. | QSC 1.1, QSC 4.1 |
| Compare and contrast different testing methods, including black and white box approaches | QSC 3.3 |
| Explain different metrics used to evaluate software complexity | QSC 1.1, QSC 4.1 |
| Outline and implement quality requirements at different phases of the software life-cycle | QSC 3.1, QSC 4.2 |
| Build a foundation for further learning by exposure to multiple computer languages, development tools, and methodologies. | QSC 6.2 |
CISC 330: Computer-Integrated Surgery
Concepts of computer-integrated surgery systems and underlying techniques such as medical-image computing, robotics, and virtual reality, learned through real-life applications and problems. Techniques learned in class will be applied in a hands-on surgery session where students perform minimally invasive surgery with virtual-reality navigation tools.
| CLO | Supports PLO(s) |
|---|---|
| Design and partly implement computer-integrated surgery systems | BMCO 3.4, QSC 1.1, QSC 3.2, QSC 3.3 |
| Analyze the mechanism of computer-integrated surgery systems | QSC 1.1, QSC 1.2, QSC 2.2 |
| Produce high-level design of computer-aided surgery systems | BMCO 3.4, QSC 1.1, QSC 3.2, QSC 3.3 |
| Implement key components in software. | BMCO 3.4, QSC 3.3 |
CISC 332: Database Management Systems
Data models: relational, entity-relationship. Relational query languages: relational algebra and SQL. Relational database design. Application interfaces and embedded SQL. Storage and indexing.
| CLO | Supports PLO(s) |
|---|---|
| Apply database design techniques and tools to create a database schema and database instance for an application. | QSC 1.1, QSC 3.1, QSC 3.2, QSC 3.3 |
| Apply an application design method and development tools to create a web application to support users in querying and manipulating the database for an application. | QSC 1.1, QSC 3.1, QSC 3.2, QSC 3.3 |
| Compose queries on a database in both SQL and the relational algebra. | QSC 3.3 |
| Assess the quality of database schemas and queries. | QSC 3.3 |
| Apply methods to improve the performance and security of a database application. | QSC 3.2 |
CISC 335: Computer Networks
Fundamental concepts in the design and implementation of computer communication networks, protocols, and applications. Overview of network architectures; applications; network programming interfaces (e.g., sockets); transport; congestion; routing and data link protocols; addressing; local area networks; wireless networks, mobility management; security.
| CLO | Supports PLO(s) |
|---|---|
| Perform basic analysis of networking protocols | QSC 1.2 |
| Use inter-process communication to develop networking protocols and applications | QSC 3.2 |
| Design and implement simple communication protocols | QSC 3.3 |
| Critically assess computer networks protocols and their applications | QSC 2.2 |
CISC 351: Advanced Data Analytics
Design and implementation of complex analytics techniques; predictive algorithms at scale; deep learning; clustering at scale; advanced matrix decompositions, analytics in the Web, collaborative filtering; social network analysis; applications in specialized domains.
| CLO | Supports PLO(s) |
|---|---|
| Design inductive model building algorithms appropriate for datasets of substantial size and complexity with ill-defined requirements | QSC 1.1, QSC 2.1, QSC 2.2, QSC 3.1, QSC 3.2, QSC 5.1 |
| Plan ways to collect data, build models, and interpret results in network datasets | QSC 3.2, QSC 3.3 |
| Evaluate the modelling performance of such algorithms, and the implications for the real-world system that the data describes | QSC 1.1, QSC 3.2, QSC 3.3, QSC 5.1 |
CISC 352: Artificial Intelligence
An introduction to the basic principles and tools of artificial intelligence. Problem solving methods and knowledge representation techniques.
| CLO | Supports PLO(s) |
|---|---|
| Apply search to game-playing and to solve Constraint Satisfaction Problems. | QSC 3.3 |
| Apply the intelligent agent paradigm as an underlying model for exploring AI problem domains, tools, and techniques. | QSC 1.1, QSC 2.1, QSC 3.3 |
CISC 360: Programming Paradigms
Review of imperatives programming features. Introduction to other widely used programming paradigms. Functional programming languages, such as LISP and Haskell. Higher order functions, lazy evaluation, abstract and recursive types, structural induction, symbolic expressions. Logic programming languages, such as PROLOG. Operational interpretation of predicates and terms, proof search, unification, backtracking. Typical applications.
| CLO | Supports PLO(s) |
|---|---|
| Design and construct simple software in both a functional language and a logic programming language. | QSC 3.2 |
| Write short programs (approximately 50 lines) in a functional language such as Haskell or LISP, including the use of recursion, lists, higher-order functions | QSC 2.2, QSC 3.2 |
| Explain the difference between lazy and eager evaluation | QSC 3.2, QSC 3.3 |
| Use structural induction to prove simple assertions about functional programs | QSC 2.2 |
| Write short programs (up to 50 lines) in a logical language such as Prolog, including lists, arithmetic, cuts and negation | COGS 3.5, COMA 3.7, QSC 2.2, QSC 3.2 |
| Predict the behavior of small programs written in either paradigm | QSC 1.2 |
| Compare and contrast the imperative, functional and logical paradigms and applications for which each is suited | QSC 3.2 |
| Build a foundation for further learning by exposure to multiple computer languages, development tools, and methodologies. | QSC 5.2 |
CISC 365: Algorithms I
Principles of design, analysis and implementation of efficient algorithms. Case studies from a variety of areas illustrate divide and conquer methods, the greedy approach, branch and bound algorithms and dynamic programming.
| CLO | Supports PLO(s) |
|---|---|
| Possess a strong understanding of computational complexity, up to and including: Polynomial time Reducibility; classes P and NP; NP-Completeness; Proofs of NP-Completeness | QSC 2.2, QSC 5.1 |
| Be able to recognize classical NP-Complete problems | QSC 5.1 |
| Be able to prove that new problems are NP-Complete using polynomial time reductions from known NP-Complete problems | QSC 2.2, QSC 5.1 |
| Apply key algorithm paradigms, both in the abstract and through concrete examples: Divide and Conquer; Greedy Algorithms; Dynamic Programming; Branch and Bound | QSC 2.1 |
| Have some understanding of an advanced topic, which varies from year to year. Recent choices have included: Linear Programming; Computational Geometry; String Matching | QSC 1.1 |
CISC 371: Numerical Optimization for Artificial Intelligence
Computational methods for artificial intelligence, particularly using numerical optimization. Applications may include: unconstrained data optimization; linear equality constraints; constrained data regression; constrained data classification; evaluating the effectiveness of analysis methods.
| CLO | Supports PLO(s) |
|---|---|
| Formulate given problems as optimization functions | QSC 2.2 |
| Synthesize data and solution methods for optimization | QSC 2.2, QSC 5.2 |
| Implement, test and evaluate optimization methods | QSC 3.3 |
| Interpret and explain methods and solutions of given problems | QSC 5.1, QSC 6.2 |
| Evaluate and critique performance of algorithms | QSC 4.1 |
CISC 422: Formal Methods in Software Engineering
Mathematical methods for describing software behaviour and structure. Topics include (but are not limited to) the following: Requirements specification. Module specification: axiomatic, algebraic, and trace specifications. Abstract models. Verification. Specification-based validation.
| CLO | Supports PLO(s) |
|---|---|
| Formalize declarative statements in propositional or predicate logic and reason about them by constructing formal proofs manually or with the help of a tool proof assistant tool | QSC 2.2, QSC 3.1 |
| Formalize structural aspects of software systems using relational logic (as offered by the Alloy tool from MIT) and reason about them using a satisfiability checker (as offered by Alloy's analyzer) | QSC 1.1, QSC 2.2, QSC 3.1 |
| Formalize behavioural aspects of software systems (such as protocols) using NuSMV's language for expressing finite state machines and reason about them using temporal logic and model checking (i.e., exhaustive state space exploration) with the NuSMV model checker. | QSC 1.1, QSC 2.2, QSC 3.1 |
| Discuss the scope and limitations of propositional logic, predicate logic, relational logic, temporal logic and finite state machines to express software systems and their properties | QSC 2.2, QSC 4.1, QSC 5.1 |
| Discuss the advantages, disadvantages, and limitations of formal proofs, satisfiability checking, and model checking to analyze software systems | QSC 4.1, QSC 5.1, QSC 5.2 |
CISC 423: Software Requirements
An integrated approach to discovering and documenting software requirements. Identification of stakeholders; customer, operator, analyst, and developer perspectives. Requirements elicitation. Transition from initial (informal) requirements to semi-formal and formal representations. Requirements analysis process; analysis patterns. Requirements specification techniques. Relation to architecture and user interface design; traceability of requirements.
| CLO | Supports PLO(s) |
|---|---|
| Explain techniques for eliciting natural language requirements from non-technical clients. | QSC 3.1, QSC 4.2 |
| Analyse natural-language requirements, resolving omissions, ambiguities, and inconsistencies. | QSC 3.1 |
| Identify both direct and indirect stakeholders for a system | QSC 4.1 |
| Elicit requirements information about the system from a variety of sources using a variety of techniques | QSC 3.1, QSC 4.1 |
| Analyze requirements information for conflict, completeness and consistency | QSC 3.1, QSC 4.1, QSC 4.2 |
| Formally model the requirements | QSC 3.1 |
| Prepare validation plans to validate the final product against the requirements | QSC 3.1 |
| Understand how requirements change over time | QSC 3.1, QSC 4.2 |
CISC 426: Real-Time Systems
Design and implementation of real-time embedded applications. Specifying timing properties: formal and semi-formal methods; soft real-time versus hard real-time. Design notations; language constructs. Real-time operating systems. Abstract device interfaces.
There are currently no learning outcomes defined for CISC 426.
CISC 432: Advanced Data Management Systems
Storage and representation of "big data", which are large, complex, structured or unstructured data sets. Provenance, curation, integration, indexing and querying of data.
| CLO | Supports PLO(s) |
|---|---|
| Create distributed storage structures for complex datasets | QSC 1.1, QSC 3.1, QSC 3.2, QSC 3.3 |
| Organize, integrate and process data from distributed storage systems | QSC 3.3 |
| Create metadata for complex datasets. | QSC 3.3 |
| Articulate issues in data provenance and curation | QSC 4.1 |
| Build workflows and query the results | QSC 3.3 |
CISC 434: Distributed Systems
Distributed systems goals, characteristics, and architectures. Processes: models, inter-process communication and coordination. Name services. Consistency and replication. Fault tolerance: design for reliable communication and recovery. Security. Development paradigms based on data types: object, file, and web-based systems.
There are currently no learning outcomes defined for CISC 434.
CISC 437: Performance Analysis
Analytic and empirical evaluation of the performance of software systems. Performance modeling. Experimental design and statistical techniques for empirical performance analysis.
There are currently no learning outcomes defined for CISC 437.
CISC 447: Introduction to Cybersecurity
An introduction to cybersecurity covering a wide range of vulnerabilities, attacks, and defense mechanisms in individual computers, networks, the Internet and the Web and applications that use them, and storage and computational clouds. The human side of cybersecurity, and the legal and ethical constraints on both attack and defense.
| CLO | Supports PLO(s) |
|---|---|
| Design cybersecurity attacks and defences for systems and networks | QSC 1.1, QSC 1.2, QSC 2.2, QSC 5.1, QSC 6.1 |
| Analyse the threat surface of a computer system or network, and design remediation mechanisms that will be effective | QSC 1.1, QSC 2.1, QSC 3.2, QSC 5.1, QSC 6.1 |
| Discuss the relationship between cybersecurity issues and wider human factors, social, political and legal frameworks | QSC 4.1, QSC 4.2, QSC 5.2, QSC 6.1 |
CISC 448: Software Reliability and Security
Software dependability and other related concepts, software process models and methods for reliable software. Software reliability engineering process, software fault tolerance and run-time monitoring. Software security engineering process, secure software design, program security vulnerabilities and software security testing
| CLO | Supports PLO(s) |
|---|---|
| Assess, prevent, and predict failures in sample software | QSC 1.2, QSC 3.1, QSC 3.2, QSC 3.3 |
| Identify and mitigate security properties of software | QSC 3.2, QSC 3.3 |
| Design, implement, and maintain failure-free software | QSC 3.1, QSC 3.2, QSC 3.3 |
| Design and implement security-aware software | QSC 3.1, QSC 3.2, QSC 3.3 |
CISC 451: Topics in Data Analytics
Content will vary from year to year; typical areas covered may include: tools for large scale data analytics (Hadoop, Spark), data analytics in the cloud, properties of large scale social networks, applications of data analytics in security.
| CLO | Supports PLO(s) |
|---|---|
| Design inductive model building algorithms appropriate for of any scale and complexity, using cutting edge technologies | QSC 1.1, QSC 2.1, QSC 3.1, QSC 3.2, QSC 3.3, QSC 5.1 |
| Plan ways to collect data, build models, and interpret results in large distributed (cloud) datasets | QSC 2.2, QSC 3.2, QSC 3.3, QSC 4.1 |
| Evaluate the modelling performance of such algorithms, and the implications for real-world system that the data describes | QSC 2.1, QSC 4.1, QSC 5.1, QSC 5.2, QSC 6.1 |
CISC 452: Neural and Genetic Computing
Introduction to neural and genetic computing. Topics include associative memory systems, neural optimization strategies, supervised and unsupervised classification networks, genetic algorithms, genetic and evolutionary programming. Applications are examined, and the relation to biologic systems is discussed.
| CLO | Supports PLO(s) |
|---|---|
| Explain the concepts behind the operation of biological neurons and the evolution of artifical neural network (ANN) to model connections as a representation of information. | COGS 3.5, QSC 1.1 |
| Design and implement different types of ANNs using a variety of design techniques and learning algorithms for prediction, clustering, classification, storage, and function approximation | QSC 2.1, QSC 3.2, QSC 3.3 |
| Apply optimization techniques such as simulated annealing and genetic algorithms with ANN training algorithms | QSC 3.2 |
| Describe with reference to recent research work how ANNs are used to simulate human cognition, vison, and memory, and are applied to intelligent systmes for language and image processing, decision support systems, and predictive systems. | QSC 4.1, QSC 6.2 |
| Explain the power and limitations of neural network systems. | QSC 5.1 |
CISC 453: Topics in Artificial Intelligence
Investigation of selected areas of artificial intelligence research. Possible topics include natural language understanding, computational perception, planning, learning, and neurocomputing.
| CLO | Supports PLO(s) |
|---|---|
| Apply artificial intelligence techniques to planning and reasoning problems. | COGS 3.5, QSC 1.1 |
| Identify representation and reasoning techniques associated with probability models | QSC 2.2 |
| Employ the full joint distribution for inference and explore the inferential advantages of knowledge of independence | QSC 2.2 |
| Apply Bayes' rule to calculate diagnostic probabilities from known causal relationships, and use Bayesian networks for exact and approximate inference | QSC 2.2 |
| Research, and present an advanced topic in artificial intelligence. | QSC 4.1, QSC 4.2, QSC 6.1, QSC 6.2 |
| Select, research, and present an advanced topic from among possibilities in Decision Making, Learning, Natural Language, Perception | QSC 4.1, QSC 4.2, QSC 6.1, QSC 6.2 |
| Formulate classical planning problems as state space search using PDDLabaction schemas | QSC 3.1, QSC 3.2, QSC 3.3 |
| Identify complexity controlling heuristics, developing them from a planning graph formulation | QSC 5.1 |
| Generalize classical planning problems to propositional logic, constraint satisfaction and first-order logic formulations | QSC 2.2 |
| Investigate relaxation of classical planning assumptions, including: time and resource constraints; limited observability; non-determinism in a Job Shop Scheduling paradigm | QSC 2.2 |
| Analyse efficiency gains through hierarchical planning approaches | QSC 5.1 |
| Summarize variations on multiactor planning scenarios, exploring related tools and techniques | QSC 3.3 |
| Extrapolate the knowledge engineering approach to include ontological issues - represent and manipulate hierarchical category representations, event calculus, modal logic for mental events and objects | QSC 2.2 |
| Explain systems for reasoning over categories and with default information | QSC 2.2, QSC 4.2 |
CISC 454: Computer Graphics
Introduction to computer graphics, including a review of current hardware; modelling and transformations in two and three dimensions; visual realism: perspective, hidden surface elimination, and shading; colour models; applications in several fields.
There are currently no learning outcomes defined for CISC 454.
CISC 455: Evolutionary Optimization and Learning
Building, applying and studying algorithms based on the Darwinian principles of natural evolution. A creative approach to AI able to create novel solutions. Genetic algorithms, evolution strategies, and genetic programming. Application to optimization and learning problems.
| CLO | Supports PLO(s) |
|---|---|
| Apply evolutionary concepts to continuous optimization problems | QSC 1.1, QSC 2.2 |
| Evaluate and choose among evolutionary computing solutions for combinatorial optimization problems | QSC 2.1, QSC 2.2 |
| Formulate and implement evolutionary algorithms for under-specified problems | QSC 2.1, QSC 3.2 |
| Present and defend designs of evolutionary algorithms | QSC 4.1 |
| Justify limitations of evolutionary concepts | QSC 5.1 |
CISC 457: Image Processing and Computer Vision
Introduction to fundamental concepts and applications in image processing and computer vision. Topics include image acquisition, convolution, Discrete Fourier Transform, image enhancement, edge detection, segmentation, image registration, human contrast perception, colour perception and reproduction, stereo vision.
There are currently no learning outcomes defined for CISC 457.
CISC 458: Programming Language Processors
Introduction to the systematic construction of a compiler: grammars and languages, scanners, top-down and bottom-up parsing, runtime organization, symbol tables, internal representations; Polish notation, syntax trees, semantic routines, storage allocation, code generation, interpreters.
| CLO | Supports PLO(s) |
|---|---|
| Use real software engineering tools to maintain and enhance existing software systems. Note: this CLLO should be rephrased in the future. | QSC 3.1, QSC 3.3 |
| Apply component-based software engineering and table-driven programming to multi-phase software systems. | QSC 3.2, QSC 3.3 |
| Follow standard practices for software quality assurance and testing in a small-team project. | QSC 4.1, QSC 4.2, QSC 6.1 |
| Employ domain-specfic languages and model-driven engineering to develop software. | QSC 3.1, QSC 3.3 |
CISC 462: Computability and Complexity
Turing machines and other models of computability such as µ-recursive functions and random-access machines. Undecidability. Recursive and recursively enumerable sets. Church-Turing thesis. Resource-bounded complexity. Complexity comparisons among computational models. Reductions. Complete problems for complexity classes.
| CLO | Supports PLO(s) |
|---|---|
| Use diagonalization to prove undecidability | QSC 2.2, QSC 5.1 |
| Use informal and formal reductions to establish algorithmic unsolvability | QSC 2.2, QSC 5.1 |
| Appraise the complexity difference between finding a solution to an algorithmic problem and verifying a solution to an algorithmic problem | QSC 2.2 |
| Use mapping reductions to establish hardness of computational problems | QSC 2.2, QSC 5.1 |
| Use hierarchy theorems to establish separation between complexity classes | QSC 2.2, QSC 5.1 |
CISC 465: Semantics of Programming Languages
Specifying syntax and semantics; operational and denotational semantics. Lambda calculi, type systems and logical foundations. Meta-theoretic properties. Semantics of imperative languages.
| CLO | Supports PLO(s) |
|---|---|
| Interpret and modify syntactic descriptions of programming languages. | QSC 1.2, QSC 2.2 |
| Interpret, modify and create rules for programming language semantics. | QSC 1.1, QSC 2.2 |
| Demonstrate connections between semantics and logics. | QSC 2.2 |
| Explain and extend standard proofs of meta-theoretic properties such as type preservation and progress. | QSC 2.2 |
CISC 466: Algorithms II
A continuation of CISC 365/3.0. Lower bound theory. Average-case analysis of algorithms. Approximation algorithms. Probabilistic algorithms. Parallel algorithms.
There are currently no learning outcomes defined for CISC 466.
CISC 468: Cryptography
Fundamentals of cryptographic algorithms: secure pseudorandom number generators, hash functions, symmetric-key cryptography (stream ciphers, block ciphers); public-key cryptography (encryption and decryption, digital signatures, key agreement). Applications of cryptography to secure communication protocols and systems.
| CLO | Supports PLO(s) |
|---|---|
| Describe underlying mathematical concepts and properties of modern cryptographic algorithms and describe their limitations. | QSC 2.2, QSC 4.1, QSC 5.1, QSC 5.2 |
| Critically evaluate the use of cryptographic algorithms in real-world computer protocols and systems to determine security objectives achieved. | QSC 1.1, QSC 3.3, QSC 4.1 |
| Demonstrate practical attacks to break vulnerable applications of cryptographic algorithms. | QSC 3.2, QSC 4.1, QSC 5.1 |
CISC 471: Computational Biology
Advanced computational approaches to the problems in molecular biology. Techniques and algorithms for sequence analysis and alignment; molecular databases; protein structure prediction and molecular data mining.
| CLO | Supports PLO(s) |
|---|---|
| Describe state-of-the-art algorithms in Bioinformatics with emphasis on the strengths and limitations of these techniques. | BMCO 3.4, QSC 1.1, QSC 2.1 |
| Distill information provided by a biologist and discern appropriate and effective algorithmic solutions. | BMCO 3.4 |
| Critically assess an algorithm for its correctness and efficiency, and argue about the algorithms correctness, performance bounds, and approximation ratio as appropriate. | |
| Make sensible pragmatic algorithmic choices of standard packages or implementations for specific applications. | QSC 3.3 |
CISC 472: Medical Informatics
Current topics in the application of information technology to medical image computing and its use in image-guided medical interventions.
| CLO | Supports PLO(s) |
|---|---|
| Analyze the inner workings of medical image manipulation and registration methods for use in image-guided computer-assisted intervention systems. | BMCO 3.4, QSC 1.1, QSC 2.2 |
| Design solutions in which modern information technology is used to solve applied problems in the medical field. | BMCO 3.4, QSC 2.1, QSC 3.3 |
| Analyze the specifics and limitations of dataflow in a medical environment, especially in the area of medical image analysis. | BMCO 3.4, QSC 5.1, QSC 5.2 |
| Design and develop basic medical image analysis and image registration methods for use in typical computer-assisted surgery and intervention applications. | BMCO 3.4, QSC 1.1, QSC 3.1, QSC 3.2, QSC 3.3, QSC 6.1 |
CISC 473: Deep Learning
Design of deep neural networks based on leading-edge algorithms such as Restricted Boltzmann Machines, Recurrent Neural Networks, Convolutional Neural Networks, Long-Short Term Machines. Autoencoding as a clustering technique. Applications to prediction problems in natural language and images.
| CLO | Supports PLO(s) |
|---|---|
| Design deep neural network predictive and clustering algorithms appropriate for natural language and image datasets of substantial size and complexity | QSC 1.1, QSC 2.1, QSC 2.2, QSC 3.1, QSC 3.2, QSC 3.3, QSC 5.1, QSC 5.2, QSC 6.1, QSC 6.2 |
| Evaluate the performance of such algorithms, and the implications for the real-world systems that the data describes | QSC 3.1, QSC 3.2, QSC 3.3, QSC 5.1, QSC 5.2, QSC 6.1, QSC 6.2 |
CISC 474: Reinforcement Learning
Formal and heuristic approaches to problem-solving, planning, knowledge representation and reasoning, Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo learning, function approximation, integration of learning and planning. Implementing simple examples of logical reasoning, clustering or classification.
| CLO | Supports PLO(s) |
|---|---|
| Understand the fundamental concepts of reinforcement learning. | QSC 2.1, QSC 2.2 |
| Apply optimization techniques to reinforcement learning. | QSC 3.2, QSC 3.3, QSC 5.1 |
| Explain applications of RL in AI. | QSC 4.1 |
| Implement RL for applications in AI, game and robotics. | QSC 1.1, QSC 2.1, QSC 3.1, QSC 3.2, QSC 3.3, QSC 6.1 |
| Understand concepts of deep RL. | QSC 1.1, QSC 2.1, QSC 5.1, QSC 5.2 |
CISC 486: Game Development
An introduction to 'engines' used in networked 3-dimensional games. Topics include game-engine architecture and components providing 3-dimensional rendering, physics simulation, sound, artificial intelligence, and networking services.
| CLO | Supports PLO(s) |
|---|---|
| Develop a simple game engine | QSC 1.1, QSC 1.2, QSC 2.2, QSC 3.1, QSC 3.2, QSC 3.3 |
| Design the software architecture of a game engine | QSC 1.1, QSC 1.2, QSC 3.1, QSC 3.2 |
| Develop engine-level algorithms supporting artificial intelligence in games | |
| Develop engine-level algorithms supporting networking in games | |
| Develop engine-level algorithms supporting physics in games |
CISC 490: Topics in Computing Science I
Content varies. Not offered every year.
There are currently no learning outcomes defined for CISC 490.
CISC 491: Topics in Computing Science II
Content varies. Not offered every year.
There are currently no learning outcomes defined for CISC 491.
CISC 492: Topics in Computing III
Content varies. Not offered every year.
There are currently no learning outcomes defined for CISC 492.
CISC 495: Software Evolution
While working in groups, students will participate in the evolution of a software artifact. Elements of a software life cycle are examined in detail: requirements analysis, design recovery, refactoring, documentation, version, and configuration management.
There are currently no learning outcomes defined for CISC 495.
CISC 496: Game Development Project
Team-based project involving the development of a game using modern tools and software engineering techniques.
| CLO | Supports PLO(s) |
|---|---|
| Design and implement a game | QSC 1.1, QSC 2.1, QSC 3.1, QSC 3.2, QSC 3.3, QSC 4.1, QSC 4.2, QSC 6.1, QSC 6.2 |
| Collaborate and communicate with colleagues to cooperatively develop a substantial software system. | QSC 4.1, QSC 4.2, QSC 6.1, QSC 6.2 |
| Schedule intermediate milestones to plan and track progress through the development process. | QSC 4.1, QSC 6.1 |
CISC 497: Social, Ethical and Legal Issues in Computing
A wide range of topics of current importance in computing, including technical issues, professional questions, and moral and ethical decisions. Students make presentations, deliver papers, and engage in discussion.
| CLO | Supports PLO(s) |
|---|---|
| Critically analyse legal, ethical and social issues related to computing | QSC 3.2, QSC 5.2, QSC 6.1, QSC 6.2 |
| Evaluate information as a guide to beliefs and action | QSC 5.2, QSC 6.1 |
| Explain cogently their position with regard to any legal, ethical or social issues | QSC 4.1, QSC 4.2 |
| Communicate effectively in writing, discussing, and presenting | QSC 4.1, QSC 4.2 |
| Evaluate social, ethical, and legal issues in computing using ethical systems and professional codes of conduct | QSC 6.1, QSC 6.2 |
CISC 498: Information Technology Project
Topic selected under the supervision of a faculty member. Emphasis is on the application of software engineering techniques to the development of a substantial software system. Group work, oral presentation, participation in design and code review meetings, and delivery of complete software specification and design are required.
| CLO | Supports PLO(s) |
|---|---|
| Collaborate and communicate with colleagues to cooperatively develop a substantial software system. | QSC 4.1, QSC 6.1 |
| Elicit and document customer requirements and develop external interfaces that satisfy them. | QSC 1.1, QSC 3.1, QSC 3.2, QSC 3.3, QSC 4.1, QSC 4.2, QSC 6.1, QSC 6.2 |
| Apply software architecture principles to divide the system into modules. | QSC 3.2, QSC 3.3 |
| Implement, validate, and deploy software in a form useful to the customer. | QSC 3.2, QSC 3.3 |
| Schedule intermediate milestones to plan and track progress through the development process. | QSC 6.1 |
CISC 499: Advanced Undergraduate Project
Students must make a project proposal and secure a supervisor prior to enrolling in this course. Emphasis may be on the development of a large program, or on more theoretical issues. Independent research, an oral presentation, and a written report are required.
| CLO | Supports PLO(s) |
|---|---|
| Demonstrate the potential for independent research and/or development via a term-long project under the supervision of a faculty member. | QSC 6.2 |
| Describe the process and results of their independent project via a written report. | QSC 4.1 |
| Summarize the key ideas of their independent project via an oral or poster presentation. | QSC 4.1, QSC 4.2 |
| Reflect on how their previous education influenced their work in the course and how their experience in the course will affect their future learning and career. | QSC 6.2 |
CISC 500: Undergraduate Thesis
Individual research project under the supervision of a School of Computing faculty member. Evaluation is based on an oral presentation and a written thesis. It is the responsibility of the student to make a research proposal and secure a supervisor prior to enrolling in the course.
| CLO | Supports PLO(s) |
|---|---|
| Demonstrate the potential for independent research via a two-term research project under the supervision of a faculty member. | QSC 1.1, QSC 6.1, QSC 6.2 |
| Describe the process and results of their independent project via a written dissertation. | QSC 4.1, QSC 4.2 |
| Summarize the key ideas of their independent project via an oral presentation. | QSC 4.1, QSC 4.2 |
COCA 201: Introduction to Computing and the Creative Arts
A multidisciplinary studio-oriented overview of computer-based applications in Art, Music, Drama, and Film. History of human-computer interaction. Critical and philosophical issues. Animation. Virtual reality. Computer-aided design. Computer games. Enrolment is limited.
| CLO | Supports PLO(s) |
|---|---|
| Design real-time interactive algorithms for user input and output. | QSC 1.1, QSC 2.1 |
| Identify user requirements for an interactive art exhibit. | QSC 3.1 |
| Communicate non-verbally with the user through the exhibit | QSC 4.1, QSC 4.2 |
| Design and construct simple real-time software and hardware systems for an effective interactive electronic arts exhibit | QSC 3.2, QSC 3.3 |
| Understand the limitations of communication with participants through art exhibits | QSC 5.2 |
| Understand the role of unpredictability in user input/output; particularly as it relates to artistic concept of discovery | QSC 5.2 |
| Understand limitations of real-time and of interactive systems. | QSC 5.1 |
| Schedule intermediate milestones to plan and track progress through the development process. | QSC 6.1 |
COGS 100: Introduction to Cognitive Science
A multidisciplinary approach to the study of the mind combining approached from philosophy, psychology, linguistics, neuroscience, anthropology, and artificial intelligence. Logic, rules, concepts, and other mental representations used to generate thought and behaviour. Implementation of computational and cognitive models of mental processes.
| CLO | Supports PLO(s) |
|---|---|
| Situate cognitive models in a practical context. | QSC 1.1, QSC 5.2 |
| Apply information-processing models to cognitive processes. | QSC 2.1 |
| Compare computer models to human behaviour. | COGS 3.5 |
| Clearly convey cognitive models to a non-technical audience. | QSC 4.1 |
COGS 201: Cognition and Computation
Introduction to the computational aspects of the mind. Implementation of computer programs for reasoning, decision making, and problem solving to understand these mental processes. Information theory and behaviourism; computational models of cognition, perception and memory processes demonstrating modeling approaches, and cognitive architectures.
| CLO | Supports PLO(s) |
|---|---|
| Illustrate theory of cognition through computational aspects such as logic propositions, reasoning, constraint satisfaction, information theory, and observable quantitative behavioural measures. | COGS 3.5, QSC 1.1 |
| Explain the two main modeling approaches: the physical symbol systems and artificial neural networks. | COGS 3.5 |
| Implement simple logic programs as physical symbol systems to simuilate human cognition and thought process in the areas of problem solving, language, and decision making | COGS 3.5, QSC 2.1, QSC 3.2 |
| Collaborate with peers to summarize and present research papers on major cognitive architectures used for modeling human cognitive processes. | QSC 4.1, QSC 4.2, QSC 6.1 |
COGS 499: Advanced Undergraduate Project
Topic selected under the supervision of a faculty member from CISC, LING, PHIL, or PSYC. Emphasis may be on experimental, theoretical, or computer implementation. Independent research, an oral presentation, and a written report are required. The student must make a project proposal and secure a supervisor prior to enrolling in the course.
| CLO | Supports PLO(s) |
|---|---|
| Demonstrate the potential for independent research and/or development via a term-long project under the supervision of a faculty member. | COGS 3.5, QSC 1.1, QSC 6.1, QSC 6.2 |
| Describe the process and results of their project via a written report. | QSC 4.1, QSC 4.2 |
| Summarize the key ideas of their project via an oral or poster presentation. | QSC 4.1, QSC 4.2 |