Introduction to algorithms: their definition, design, coding, and execution on computers. Intended for students who have no programming experience. All or most assignment work will be completed during lab time.
NOTE Also offered online. Consult Arts and Science Online. Learning Hours may vary. Sufficient preparation for CISC 121; alternative to CISC 110/3.0 and CISC 151/3.0
LEARNING HOURS 120(36L;84P)
Introduction to mathematical discourse and proof methods. Sets, functions, sequences, and relations. Properties of the integers. Induction. Equivalence relations. Linear and partial orderings.
NOTE Also offered online. Consult Arts and Science Online. Learning Hours may vary.
LEARNING HOURS 120 (36L;84P)
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.
NOTE No computing or art background required.
NOTE Sufficient preparation for CISC 121/3.0. . Alternative to CISC 151/3.0. With permission of the School, students with programming experience may take this concurrently with CISC 121/3.0
LEARNING HOURS 120 (36L;84P)
Introduction to design, analysis, and implementation of algorithms. Recursion, backtracking, and exits. Sequences. Elementary searching and sorting. Order-of-magnitude complexity. Documentation, iterative program development, translating natural language to code, testing and debugging.
NOTE Also offered online. Consult Arts and Science Online. Learning Hours may vary.
LEARNING HOURS 120 (36L;84P)
RECOMMENDATION Some programming experience (such as high-school level programming or CISC 101/3.0 or CISC 110/3.0 or CISC 151/3.0)
Introduction to object-oriented design, architecture, and programming. Use of packages, class libraries, and interfaces. Encapsulation and representational abstraction. Inheritance. Polymorphic programming. Exception handling. Iterators. Introduction to a class design notation. Numerical computation. Applications in various areas.
LEARNING HOURS 120 (36L;24Lb;60P)
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.
NOTE Sufficient preparation for CISC 121/3.0. Alternative to CISC 101/3.0 and CISC 110/3.0
LEARNING HOURS 120(36L;84P)
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.
LEARNING HOURS 120 (36L;84P)
Proof methods. Combinatorics: permutations and combinations, discrete probability, recurrence relations. Graphs and trees. Boolean and abstract algebra.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P)
Introduction to design and analysis of algorithms. Control structures: recursion, backtracking. Data structures: multidimensional arrays, defined data types. Introduction to numerical computation. Documentation, testing and debugging. Object-oriented design, architecture and programming: encapsulation and representational abstraction, inheritance and polymorphism, interfaces and abstract classes, exception handling. (0/0/0/26/22)
Basic concepts of Unix-like systems. Shells and scripting. System-level programming in the C language. Software development tools and techniques.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (12L;24G;84P)
RECOMMENDATION CISC 220/3.0.
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.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;60G;24P)
Design and implementation of advanced data structures and related algorithms, including correctness and complexity analysis.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;24Lb;60P).
Elements of linear algebra for data analysis, including: solution of linear equations; vector spaces; matrix decompositions; principal components analysis; linear regression; hyperplane classification of vectorial data.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;48O;36P)
Introduction to management of small and medium-scale software projects. Advanced programming methodology using the programming language C++. Includes a significant programming project.
LEARNING HOURS 120 (36L;24T;24G;36P)
Abstractions and patterns of interactions and relationships among modules. Design recovery; relationship of architecture to requirements and testing.
LEARNING HOURS 120 (36L;24T;36G;24P)
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.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P)
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
LEARNING HOURS 120 (36L;24T;24G;36P).
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).
LEARNING HOURS 120 (36L;84G)
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.
LEARNING HOURS 120 (36L;84P)
Data models: relational, entity-relationship. Relational query languages: relational algebra and SQL. Relational database design. Application interfaces and embedded SQL. Storage and indexing.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P)
Combinational circuits; sequential circuits; digital systems design; micro-programming; bus structures; data communications; interface design; microprocessor systems.
LEARNING HOURS 120 (12L;24G;84P)
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.
LEARNING HOURS 120 (36I;36Lb;84P)
An introduction to the basic principles and tools of artificial intelligence. Problem solving methods and knowledge representation techniques.
LEARNING HOURS 120 (36L;84P)
RECOMMENDATION CISC 360/3.0 or CISC 260/3.0.
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.
LEARNING HOURS 120 (36L;84P)
EQUIVALENCY CISC 260/3.0.
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.
LEARNING HOURS 120 (36L;84P)
Methods for nonlinear data analysis, 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.
LEARNING HOURS 120 (36L;84P)
Inductive modelling of data, especially counting models; ensemble approaches to modelling; maximum likelihood and density-based approaches to clustering, visualization. Applications to non-numeric datasets such as natural language, social networks, Internet search, recommender systems. Introduction to deep learning. Ethics of data analytics.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P)
Advanced user-interface styles such as eye-tracking input, digital desks, wearable computing, ubiquitous and context-aware computing, and tangible interfaces.
LEARNING HOURS 120 (36L;84P)
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.
Storage and representation of "big data", which are large, complex, structured or unstructured data sets. Provenance, curation, integration, indexing and querying of data.
LEARNING HOURS 120 (36L;84P).
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.
LEARNING HOURS 120 (36L;84P)
Analytic and empirical evaluation of the performance of software systems. Performance modeling. Experimental design and statistical techniques for empirical performance analysis.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120(36L;84P)
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
LEARNING HOURS 120(36L;84P)
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.
LEARNING HOURS 120 (36I;36Lb;48P)
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.
LEARNING HOURS 120 (36L;84P)
Investigation of selected areas of artificial intelligence research. Possible topics include natural language understanding, computational perception, planning, learning, and neurocomputing.
LEARNING HOURS 120 (36L;84P)
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. James Stewart.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P).
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.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;36Lb;48G)
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.
LEARNING HOURS 120 (36L;84P)
RECOMMENDATION CISC 365/3.0.
Specifying syntax and semantics; operational and denotational semantics. Lambda calculi, type systems and logical foundations. Meta-theoretic properties. Semantics of imperative languages.
LEARNING HOURS 120 (36L;84P)
A continuation of CISC 365/3.0. Lower bound theory. Average-case analysis of algorithms. Approximation algorithms. Probabilistic algorithms. Parallel algorithms.
LEARNING HOURS 120 (36L;84P)
History of fuzzy theory; fundamental concepts of fuzzy theory: sets, relations, and logic operators. Approximate reasoning, fuzzy inference, possibility theory. Separation from probability. Fuzzy control systems. Fuzzy pattern recognition. Advanced topics may include fuzzy expert systems, financial systems, graph theory, optimization.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P).
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.
LEARNING HOURS 120 (36L;84P)
Current topics in the application of information technology to medical image computing and its use in image-guided medical interventions.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;84P)
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.
LEARNING HOURS 120 (36L;12G;72P)
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.
LEARNING HOURS 120 (36L;15G;69P)
Content varies. Not offered every year.
NOTE Learning Hours will vary.
Content varies. Not offered every year.
NOTE Learning Hours will vary.
Content varies. Not offered every year.
NOTE Learning Hours will vary.
Team-based project involving the development of a game using modern tools and software engineering techniques.
LEARNING HOURS 129 (9L;120G)
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.
LEARNING HOURS 120 (12L;24S;84P)
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.
LEARNING HOURS 258 (18S;240G)
Topic selected under the supervision of a faculty member. 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.
LEARNING HOURS 120 (120P)
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.
LEARNING HOURS 240 (24I;216P)
The objective of this course is to provide students hands-on training in computer programming languages and tools to familiarize them with the principles and practice of cutting edge technologies for bioinformatics used in biomedical and molecular sciences research. This course will span over four weeks and will consist of a combination of lectures and interactive computer labs. Specifically, students will acquire working knowledge of basic functions in Rand MATLAB for managing and pre- processing biomedical data, and use examples of real biomedical data to apply methods for identifying data outliers and gain skills for cloud computing. PREREQUISITE: Admission in the Graduate Diploma (GDip [BI]) or Masters in Biomedical Informatics (MBI) or permission of the Program Coordinator. EXCLUSION: None
The objective of this course is to provide students with hands-on training in the analysis of biomedical datasets to familiarize them with the principles and practice of cutting edge technologies for bioinformatics used in biomedical and molecular sciences research. This course will span over four weeks and will consist of a combination of lectures and interactive computer labs. Specifically, students use examples of real biomedical data to apply methods for recognizing patterns, classify and extract features using supervised and unsupervised learning approaches, and grasp basic concepts in biostatistics and employ statistical algorithms for data analysis. PREREQUISITE: CISC 801 and admission in the Diploma in Biomedical Informatics or Masters in Biomedical Informatics (MBI) or permission of the Program Coordinator. EXCLUSION: None
The objective of this course is to provide students with hands-on training in data mining to familiarize them with the principles and practice of cutting edge technologies for bioinformatics used in biomedical and molecular sciences research. This course will span over four weeks and will consist of a combination of lectures and interactive computer labs. Specifically, students will acquire knowledge and skills for mining various biomedical dataset, survey the breadth/depth of biomedical datasets and how they are used to advance biomedical research, design an experiment that will require them to generate a hypothesis and test that hypothesis using real biomedical data. PREREQUISITE: CISC 801, 802 and admission in the Diploma in Biomedical Informatics or Masters in Biomedical Informatics (MBI) or permission of the Program Coordinator. EXCLUSION: None
This course is offered in the Fall semester and is open to graduate students and advanced undergraduate students interested in Computational Science and Engineering. The purpose of this course is to present the tools necessary to pursue computational science and engineering within the student's 'home' discipline. This course is offered jointly with QCSE 810*. Graduate students enrolled in the CSE specialization must register in QCSE 810*. The Queen's CSE management committee may cap enrollment, in which case students in the CSE specialization have priority.
This course is offered in the Winter semester and is open to graduate students and advanced undergraduate students interested in Computational Science and Engineering. This course covers the principles and practices of current high performance computing architecture and software, such as parallel, massively parallel, and grid computing. Advanced tools for analyzing and optimizing the performance of algorithms important to computational science and engineering will be introduced. This course is offered jointly with QCSE 811*. Graduate students enrolled in the CSE specialization must register in QCSE 811*. The Queen's CSE management committee may cap enrollment, in which case students in the CSE specialization have priority.
This course introduces the different paradigms of wireless and mobile networking. It will also familiarize students with the state-of-the-art technologies, as well as with recent efforts in research and standardization. Students taking this course will develop a strong grasp of the different architectures involved in current and future wireless networks, have a functional understanding of the different paradigms, design and analyze the behavior of wireless and mobile networks protocols using one or more network simulation tools. PREREQUISITE: CISC-435 or equivalent.
Theory and practice of modern data base systems; data as a model of reality; architecture of current and proposed systems. Networks models, entity data model and relational models of data. Data independence, security, data base integrity, contention handling, data definition languages, data manipulation languages and their relation to current and proposed systems. Readings from current research literature. Two term-hours; lectures. Two term-hours; lectures. Pat Martin.
This course considers operating systems for distributed architectures covering the following topics: distributed system characteristics, process synchronization and communication, basic distributed algorithms, principles of fault tolerance, reliable broadcast, naming, file systems, load balancing, and layering. Three term-hours; lectures and seminars.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars. Hossam Hassanein.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars. Ahmed Hassan.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars. Juergen Dingel
The course describes fundamental techniques, principles and technologies associated with network traffic and Quality of Service (QoS) management. The Broadband Integrated Services architecture and Asynchronous Transfer Mode are described. TCP/IP flow and congestion control and the Integrated and Differentiated Services architectures and their accompanying protocols and techniques for QoS management are detailed. The course includes studies of end-to-end resource management across heterogeneous domains and platforms. H. Hassanein
Designing and constructing large software system: requirements analysis, architectural design, detailed design and specification, implementation, test planning, delivery, evolution. Project and configuration management issues. This course includes a team project. Three term-hours; lectures and seminars.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars.
Large uniprocessor design, including pipelining, cache management, lookahead and prefetch; array processors and algorithms; multi-processors, conventional multiprocessors, systolic arrays, data driven and demand driven architectures. Three term-hours; lectures and seminars.
A study of the techniques necessary for developing complete parallel systems. This includes program development techniques in a parallel setting, parallel languages, compilation and transformation techniques, and properties of parallel architectures. The emphasis is on integrating solutions to each of these stages to provide practical parallel systems. Three term-hours; lectures and seminars.
Mining Software Repositories, Applied Machine Learning in Software Engineering, Automated Software Engineering, Empirical Software Engineering, Software Engineering for Artificial Intelligence.
PREREQUISITE: Permission of the School of Computing
Software system engineering. Essential elements of requirements. Natural and formal language specification. Software process models. Mathematical foundations. Model-oriented specification styles: informal (data flow, control flow, process-based, data models, object-oriented); operational (process-based, state machine, data models); set theory and logic. Property-oriented specification style: trace-based; event-based; abstract data type. Refinement of requirements to components. Tool support. Case studies. Three term-hours; lectures.
General characteristics of designs and design methods. Survey of design methods: information hiding; object-oriented design; state machine methods; data flow design. Module specification techniques: first-order logic; algebraic specification; trace specification; weakest preconditions; the refinement calculus; abstract modeling. Transition from design to implementation. Three term-hours; lectures and seminars.
Formal techniques: proving programs correct, checking consistency and completeness. Inspections and reviews. Unit/module testing. White box and black box testing. System integration and testing. Tool support for testing. Faults vs. failures. Verification of implementation against both requirements and design. Techniques for critical software. Trust-worthiness vs. reliability. Timing analysis and verification. Safety analysis. Multi-version programming. Software quality assurance, software reliability. Debugging. Three term-hours; lectures and seminars.
Software crisis and software process models, Software reliability and methods for reliable software, Software reliability engineering process, Software dependability, Software fault tolerance, Run-time software monitoring, Software security, Software security engineering process, Network security, Intrusion detection. Three term hours: lectures and seminars.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the department. Three term-hours; lectures and seminars. Diane Kelly.
This course offers a hands-on introduction to evolutionary computing, a field that amounts to building, applying and studying algorithms based on the Darwinian principles of natural evolution. Evolutionary computing, as a population-based search technique inspired by evolution, is able to create novel solutions and is often regarded as a creative approach to AI. Students will learn various evolutionary computing techniques including genetic algorithms, evolution strategies, and genetic programming, and will study their applications to optimization and learning problems.
PREREQUISITE: Permission of the School of Computing
Expressing concurrent systems using finite-state machines and Buechi automata. Expressing properties of systems: assertions, safety properties, liveness properties, temporal logic, specification patterns. Analyzing concurrent systems using model checking: algorithms, complexity, and optimizations. Academic and industrial model checking tools. Three term hours: lectures and seminars.
Advanced computer graphics with emphasis on physical modelling and rendering of realistic 3-d scenes. Topics include constructive solid geometry, parametric shape representations, kinematics and dynamics, photometry and surface reflectance, and ray tracing. Three term-hours; lectures and seminars. James Stewart.
Methods for computational optimization, particularly examining nonlinear functions of vectors. Topics may include: unconstrained optimization; first-order methods; second-order methods; convex problems; equality constraints; inequality constraints; applications in machine learning.
This course includes topics on formal and heuristic approaches to problem solving, planning, reinforcement learning, knowledge representation and
reasoning, Markov decision processes, dynamic programming, temporal difference learning, Monte Carlo reinforcement learning methods, function approximation methods, integration of learning and planning.
Examines the techniques used in digital image processing. Topics covered include image transformations (FFT, DCT, wavelets), linear and nonlinear filtering, image compression, image restoration and enhancement, Bayesian estimation of image properties, and image registration methods. Applications examined include medical imaging, motion analysis, remote sensing imagery (satellite and aircraft), and industrial imaging (inspection). Three term-hours; lectures and seminars.
An introduction to the systematic construction of a compiler: grammars and languages; scanners; top-down and bottom-up parsing; run-time organization; symbol tables; internal representations; Polish notation; syntax trees; semantic routines; storage allocation; code generation; interpreters. Three term-hours; lectures. Jim Cordy.
An introduction to statistical and structural pattern recognition. Feature extraction and the feature space. Bayes decision theory. Parametric classification. Clustering methods. Syntactic pattern description: string, tree and graph grammers; attributed grammars; stochastic grammars. Error correcting parsing; parsing of stochastic languages. Assignments include practical experience in application areas such as character recognition and document image analysis. Three term-hours; lectures and seminars. Dorothea Blostein.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars.
The course will cover historical and recent research in computer graphics. The instructor will give lectures on the physics of illumination, radiosity methods for light transport, Monte Carlo methods for light transport, visibility, and geometric problems. Students will give presentations on papers that they've chosen from the current research literature. Three term hours: lectures and seminars. James Stewart.
The language of while programs. Programming logics. Operational and denotational semantics. The typed and untyped lambda calculi. Recursive definitions and domain theory. Full abstraction. Three term-hours; lectures and seminars. Bob Tennent.
An introduction to cybersecurity covering a wide range of vulnerabilities, attacks, and defence 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 defence.
Teaches algorithms and concepts about deep learning based on the biological neural network. Students will learn about deep belief network, restricted Boltzmann machine, Convolutional, Generative adversarial and Long Short Term Recursive NN and develop DNN using tools such as TensorFlow to perform feature extraction, image recognition and text processing.
A study of fundamental techniques for developing effective algorithms and data structures to solve geometric problems. Topics include - algorithms for computing convex hulls, triangulations, minimum spanning circles, Voronoi diagrams; point location problems; intersection problems; path planning, and hidden surface algorithms. David Rappaport.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars.
An introduction to graph theory, and a survey of graph theoretic algorithms as applied to classic and contemporary problems in combinatorics and computer science. Topics include: colouring, isomorphism, connectivity, network flow, matchings, planarity, shortest path problems, NP- completeness. Three term-hours; lectures and seminars. Robin Dawes.
The design and analysis of parallel algorithms. Computational models. Complexity classes. Parallel algorithms for various problems including: basic arithmetic, sorting, searching, selection, graph theory, matrix computations, combinatorial enumeration, optimization, computational geometry, and numerical analysis. Three term hours; lectures and seminars.
Study of the extraction of concepts from large high-dimensional datasets. Statistical foundations; techniques such as supervised neural networks, unsupervised neural networks, decision trees, association rules, Bayesian classifiers, inductive logic programming, genetic algorithms, singular value decomposition, hierarchical clustering. Three term hours; lectures and seminars. David Skillicorn.
Theoretical foundation and practical applications of Artificial Neural Networks (ANN) and Cognitive Computing (CC) models. Paradigms of neural computing algorithms using attention and context embedding models, applications in cognitive modeling, artificial intelligence, and machine learning with multi-stream data processing techniques.
PREREQUISITE: Permission of the School of Computing
This inter-disciplinary course for students in the computational and life sciences looks at the application of computing techniques to molecular biology. Topics may include: DNA data analysis (genomics), secondary and tertiary structure analysis (nucleic acids and proteins), molecular scene analysis, evolutionary trees (phylogenetics), and computing with DNA. Three term hours; lectures and seminars.
A systematic studyof the known relations between the most important resource bounded complexity classes. Reductions, separation results and translation techniques. Relativized computation. Topics may include also: Circuit complexity, Kolmogorov-Chaitin complexity, Communication complexity. Three term-hours; lectures and seminars. Kai Salomaa.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars. Nick Graham.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars. Selim Akl.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term-hours; lectures and seminars. Ahmed Hassan.
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term hours: lectures and seminars
Consists of formal lectures and the study and discussion of research papers appearing in the current literature. Students will be expected to participate in the presentation of the lecture material. Topics chosen for study will be by arrangement with the School. Three term hours: lectures and seminars. PREREQUISITE: Permission of the School.
Cloud computing is a distributed computing paradigm where computing resources are provided in an on-demand manner. The goal of the courses is to introduce students to key concepts and techniques from cloud computing. The course focuses on issues such as system architectures, resource allocation and management, and approaches and systems for the storage, management and processing of data in cloud environments.
This is a topics-based course that presents a comprehensive set of research within the broad range of activities in Human-Computer Interaction (HCI). Topics include eye-tracking input, digital desks, wearable computing, ubiquitous and context-aware computing, tangible interfaces and organic user interfaces. Each area will be treated in depth, on the basis of its scientific foundations. In addition, you will get important knowledge on the body of scientific work in HCI, practice of scientific evaluation, practice in advanced engineering with project-based deliverables. PREREQUISITE: CISC-325, CISC 425 or permission of the instructor.
Designed to develop professional skills that complement students' qualifications and technical skills, and provides multidisciplinary skills relevant to industrial and academic careers in cybersecurity. These skills include aspects of intellectual property/commercialization; leadership and management; social responsibility; communication; and public relations. The course includes completing external workshops and certifications (e.g., Queen's Expanding Horizons workshops, MITACS STEP workshops), and participating in either a cyber red on blue exercise, or a tabletop cyber strategic thinking exercise. This course is graded on a Pass/Fail basis.
This course provides an introduction to the primary and secondary sources of information in the computing science literature. The course includes work aimed at improving research skills. Students are required to submit and present a paper on a topic that relates to their research. Janice Glasgow.
A major programming project is undertaken under the supervision of a School member. The presentation of a seminar to describe the project is required.