2019-2020 Software Engineering Technical Complementaries

Computer Science (Sci): Finite automata, regular languages, context-free languages, push-down automata, models of computation, computability theory, undecidability, reduction techniques.

Offered by: Computer Science

• 3 hours
• Prerequisite: COMP 251.

• Terms
  • Fall 2019
• Instructors
  • Claude Crepeau

Computer Science (Sci): Computer representation of numbers, IEEE Standard for Floating Point Representation, computer arithmetic and rounding errors. Numerical stability. Matrix computations and software systems. Polynomial interpolation. Least-squares approximation. Iterative methods for solving a nonlinear equation. Discretization methods for integration and differential equations.

Offered by: Computer Science

• 3 hours
• Prerequisites: MATH 222 and MATH 223 and one of: COMP 202, COMP 208, COMP 250; or equivalents.
• Restrictions: Students cannot receive credit for both COMP 350 and MATH 317.

• Terms
  • Fall 2019
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Computer Science (Sci): Characteristics and utility of concurrent programs; formal methods for specification, verification and development of concurrent programs; communications, synchronization, resource allocation and management, coherency and integrity.

Offered by: Computer Science

• 3 hours
• Prerequisites: COMP 251, COMP 302, and COMP 310 or ECSE 427

• Terms
  • Winter 2020
• Instructors
  • Clark Verbrugge

Computer Science (Sci): This course considers issues relevant to the design of robotic and of intelligent systems. How can robots move and interact. Robotic hardware systems. Kinematics and inverse kinematics. Sensors, sensor data interpretation and sensor fusion. Path planning. Configuration spaces. Position estimation. Intelligent systems. Spatial mapping. Multi-agent systems. Applications.

Offered by: Computer Science

• 3 hours
• Prerequisites: COMP 251, MATH 223 and (ECSE 321 or COMP 206).

• Terms
  • Fall 2019
• Instructors
  • David P Meger

Computer Science (Sci): Introduction to search methods. Knowledge representation using logic and probability. Planning and decision making under uncertainty. Introduction to machine learning.

Offered by: Computer Science

• 3 hours
• Prerequisites: (COMP 206 or ECSE 321), MATH 323 or equivalent and COMP 251.

• Terms
  • Winter 2020
• Instructors
  • Jackie Cheung

Computer Science (Sci): Models and Architectures. Application-oriented communication paradigms (e.g. remote method invocation, group communication). Naming services. Synchronization (e.g. mutual exclusion, concurrency control). Fault-tolerance (e.g. process and replication, agreement protocols). Distributed file systems. Security. Examples of distributed systems (e.g. Web, CORBA). Advanced Topics.

Offered by: Computer Science

• 4 hours
• Prerequisites: COMP 310, COMP 251 or equivalent.

• Terms
  • This course is not scheduled for the 2019 academic year
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Computer Science (Sci): The structure of a compiler. Lexical analysis. Parsing techniques. Syntax directed translation. Run-time implementation of various programming language constructs. Introduction to code generation for an idealized machine. Students will implement parts of a compiler.

Offered by: Computer Science

• 3 hours, 1 hour consultation
• Prerequisites: COMP 273 and COMP 302

• Terms
  • Winter 2020
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Computer Science (Sci): Genre and history of games, basic game design, storytelling and narrative analysis, game engines, design of virtual worlds, real-time 2D graphics, game physics and physical simulation, pathfinding and game AI, content generation, 3D game concerns, multiplayer and distributed games, social issues.

Offered by: Computer Science

• 4 hours
• Prerequisite: COMP 251, MATH 223 and (COMP 303 or COMP 361).

• Terms
  • Fall 2019
• Instructors
  • Clark Verbrugge

Computer Science (Sci): Propositional logic - syntax and semantics, temporal logic, other modal logics, model checking, symbolic model checking, binary decision diagrams, other approaches to formal verification.

Offered by: Computer Science

• 3 hours
• Prerequisites: COMP 251 and COMP 330.

• Terms
  • This course is not scheduled for the 2019 academic year
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Computer Science (Sci): Development, analysis, and maintenance of software architectures, with special focus on modular decomposition and reverse engineering.

Offered by: Computer Science

• 4 hours
• Prerequisite: COMP 303.

• Terms
  • This course is not scheduled for the 2019 academic year
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Computer Science (Sci): Model-driven software development; requirements engineering based on use cases and scenarios; object-oriented modelling using UML and OCL to establish complete and precise analysis and design documents; mapping to Java. Introduction to meta-modelling and model transformations, use of modelling tools.

Offered by: Computer Science

• 3 hours
• Prerequisite: ECSE 321 or COMP 303 or COMP 361

• Terms
  • This course is not scheduled for the 2019 academic year
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Computer Science (Sci): Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.

Offered by: Computer Science

• Prerequisite(s): MATH 323 or ECSE 205 or ECSE 305 or equivalent
• Restriction(s): Not open to students who have taken COMP 598 when topic was "Applied Machine Learning"
• Some background in Artificial Intelligence is recommended, e.g. COMP-424 or ECSE-526, but not required.

• Terms
  • Fall 2019
  • Winter 2020
• Instructors
  • William L Hamilton

Computer Science (Sci): Fundamental mathematical and computational issues in computer animation with a focus on physics based simulation: overview of numerical integration methods, accuracy and absolute stability, stiff systems and constraints, rigid body motion, collision detection and response, friction, deformation, stable fluid simulation, use of motion capture, and other selected topics.

Offered by: Computer Science

• Prerequisite(s): MATH 222, MATH 223, COMP 206, COMP 250

• Terms
  • Winter 2020
• Instructors
  • Paul Kry

Computer Science (Sci): Study of a collection of algorithms that are basic to the world of concurrent programming. Discussion of algorithms from the following areas: termination detection, deadlock detection, global snapshots, clock synchronization, fault tolerance (byzantine and self-stabilizing systems). Students will implement algorithms on the BBN butterfly and will present papers on topics in these areas.

Offered by: Computer Science

• 3 hours
• Prerequisite: COMP 310

• Terms
  • This course is not scheduled for the 2019 academic year
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Electrical Engineering: Design of digital systems. Implementation technologies; arithmetic modules; synthesis and advanced modelling techniques; verification; timing analysis; design for testability; asynchronous circuits; hardware/software co-design.

Offered by: Electrical & Computer Engr

• Prerequisite(s): ECSE 324
• (3-2-4)

• Terms
  • Winter 2020
• Instructors
  • Zeljko Zilic

Electrical Engineering: An introduction to the automated processing, analysis, and understanding of image data. Topics include image formation and acquisition, design of image features, image segmentation, stereo and motion correspondence matching techniques, feature clustering, regression and classification for object recognition, industrial and consumer applications, and computer vision software tools.

Offered by: Electrical & Computer Engr

• Prerequisite(s):(ECSE 304 or ECSE 306 or ECSE 206 or ECSE 316) and ECSE 205.
• (3-1-5)

• Terms
  • Fall 2019
  • Winter 2020
• Instructors
  • Tal Arbel
  • Tal Arbel

Electrical Engineering: Architecture and protocols of contemporary networks; wired and wireless access systems; flow and congestion control; network optimization; randomized multiple access protocols; queueing disciplines; low-power wireless networks. Examples: Ethernet, TCP/IP, 802.11, 802.15.4. Lab experiments addressing routing protocols, TCP, queuing disciplines and quality-of-service, and network security.

Offered by: Electrical & Computer Engr

• Prerequisite(s): COMP 250, ECSE 205, and either ECSE 308 or ECSE 316.
• Restriction(s): Not open to students who have taken ECSE 414 or ECSE 489.
• (3-4-5)

• Terms
  • Fall 2019
• Instructors
  • Mark J Coates

Electrical Engineering: Definition, structure and properties of embedded systems. Real-time programming: interrupts, latency, context, re-entrancy, thread and process models. Microcontroller and DSP architectures, I/O systems, timing and event management. Real-time kernels and services. Techniques for development, debugging and verification. Techniques for limited resource environments. Networking for distributed systems.

Offered by: Electrical & Computer Engr

• (3-1-5)
• Prerequisites: ECSE 322 and ECSE 323 or ECSE 324

• Terms
  • Winter 2020
• Instructors
  • Jeremy Cooperstock

Electrical Engineering: Introduction to fault-tolerant systems. Fault-tolerance techniques through hardware, software, information and time redundancy. Failure classification, failure semantics, failure masking. Exception handling: detection, recovery, masking and propagation, termination vs. resumption. Reliable storage, reliable communication. Process groups, synchronous and asynchronous group membership and broadcast services. Automatic redundancy management. Case studies.

Offered by: Electrical & Computer Engr

• (3-1-5)
• Prerequisites: ECSE 322 or ECSE 324 and COMP 250

• Terms
  • Winter 2020
• Instructors
  • Muthucumaru Maheswaran

Electrical Engineering: The course highlights human-computer interaction strategies from an engineering perspective. Topics include user interfaces, novel paradigms in human-computer interaction, affordances, ecological interface design, ubiquitous computing and computer-supported cooperative work. Attention will be paid to issues of safety, usability, and performance.

Offered by: Electrical & Computer Engr

• (3-4-2)
• Prerequisites: ECSE 322 or (ECSE 324 and COMP250) or (COMP 251 and COMP 273)

• Terms
  • Fall 2019
• Instructors
  • Jeremy Cooperstock

Electrical Engineering: Trends in technology. CISC vs. RISC architectures. Pipelining. Instruction level parallelism. Data and Control Hazards. Static prediction. Exceptions. Dependencies. Loop level paralleism. Dynamic scheduling, branch prediction. Branch target buffers. Superscalar and N-issue machines. VLIW. ILP techniques. Cache analysis and design. Interleaved and virtual memory. TLB translations and caches.

Offered by: Electrical & Computer Engr

• (3-1-5)
• Prerequisites: (ECSE 322 and ECSE 323) or ECSE 324
• Tutorials assigned by instructor.

• Terms
  • Winter 2020
• Instructors
  • Amin Emad

Electrical Engineering: Design, development, and implementation of code integration processes, release pipelines, and deployment strategies.

Offered by: Electrical & Computer Engr

• (3-2-4)
• Prerequisite(s): ECSE 321 or COMP 303
• Restrictions: Restricted to students majoring in Software Engineering.

• Terms
  • Fall 2019
• Instructors
  • Shane McIntosh

Electrical Engineering: Practical and theoretical knowledge for developing software languages and models; foundations for model-based software development; topics include principles of model-driven engineering; concern-driven development; intentional, structural, and behavioral models as well as configuration models; constraints; language engineering; domain-specific languages; metamodeling; model transformations; models of computation; model analyses; and modeling tools.

Offered by: Electrical & Computer Engr

• (3-2-4)
• Prerequisites: ECSE 321 or COMP 303.
• Restricted to Software Engineering students.

• Terms
  • This course is not scheduled for the 2019 academic year
• Instructors
  • There are no professors associated with this course for the 2019 academic year

Electrical Engineering: Design techniques for developing modern microprocessor-based systems, multiple state-of-art instructions set architectures (ISAs) and associated assembly languages, use of tools for compiling, linking, memory overlay; debug techniques for start-stop and real-time debugging, together with debug infrastructure and interfaces: flash patching, variable watching and instruction stream tracing. Use of coprocessors and computer peripherals, such as SPI, I2C, I2S, SAI, USB, wireless standards, timers, DMA units and FLASH accelerators. Interfacing and processing sensor data including multi-sensor integration. Design techniques that promote structured approaches for separation of concerns in computing and communication. Real-time systems and software engineering for tightly integrated hardware.

Offered by: Electrical & Computer Engr

• Prerequisite(s): ECSE 324
• Restriction(s): Not open to students who have taken ECSE 426.
• (3-4-5)

• Terms
  • Fall 2019
  • Winter 2020
• Instructors
  • Brett Meyer
  • Brett Meyer

Electrical Engineering: Introduction to mathematical models of light transport and the numerical techniques used to generate realistic images in computer graphics. Offline (i.e., raytracing) and interactive (i.e., shader-based) techniques.

Offered by: Electrical & Computer Engr

• (3-2-4)
• Prerequisites: ECSE 202, ECSE 205, and COMP 250
• Restrictions: Not open to students who have taken or are taking ECSE 546.

• Terms
  • Fall 2019
• Instructors
  • Derek Nowrouzezahrai

Electrical Engineering: Fundamental mathematical, algorithmic and representational issues in computer graphics: overview of graphics pipeline, homogeneous coordinates, projective transformations, line-drawing and rasterization, hidden surface removal, surface modelling (quadrics, bicubics, meshes), rendering (lighting, reflectance models, ray tracing, texture mapping), compositing colour perception, and other selected topics.

Offered by: Electrical & Computer Engr

• (3-0-9)
• Prerequisite: ECSE 324

• Terms
  • This course is not scheduled for the 2019 academic year
• Instructors
  • There are no professors associated with this course for the 2019 academic year