Software Engineering

Regression, Classification and Decision Theory, Bias-Variance Trade-off, Linear Models, Kernel
Methods, Probabilistic Models, Neural Networks, Model Aggregation, Unsupervised Learning.

PREREQUISITE(S):
One of COMPSCI 2C03 or SFWRENG 2C03 or SFWRENG 2MD3.
One of STATS 1L03, STATS 2D03, STATS 3Y03 is recommended.
One of COMPSCI 4O03 or 4X03 or SFWRENG 3O03 or 4X03 is recommended.

Development and analysis of simple algorithms. Implementation of algorithms in computer programming language. Design and testing of computer programs.

This course introduces several concepts used in systems engineering, predictive control and artificial intelligence. A variety of techniques including prediction and estimation, linear models, basic optimization techniques, Monte Carlo techniques, neural networks, and clustering are introduced. The techniques are applied to predictive and smart systems by the example of model predictive control and intelligent control, classification and decision-making. The course is intended for engineering students with understanding in signals and systems and control.
Three lectures and one tutorial; one term
Prerequisite(s): MECHTRON 3DX4 or SFWRENG 3DX4 or IBEHS 4A03
Offered on an irregular basis.

Functions, relations and sets; the language of predicate logic, propositional logic; proof techniques, counting principles; induction and recursion, discrete probabilities, graphs, and their application to computing.

Finite state automata and grammars, predicate logic and formal proofs, models of computation, complexity, modular arithmetics, and their applications to computing.

Instruction-set architecture, computer arithmetic, datapath and control, pipelining, memory hierarchies, I/O systems, multiprocessor systems, graphic processors, measures of performance.

Fundamental concepts of programming: expressions, statements, procedures, control structures, iteration, recursion, exceptions; basic data structures: records, arrays, dynamic structures; use of libraries.

Unix and shell programming, makefiles, version control; assembly basics, translating high-level language into assembly, parameter passing, arrays, recursion; compiling, debugging, profiling, and software optimizations.

Open-ended design of computational solutions to practical problems that involve both theoretical (algorithmic) analysis and implementation; solving computational problems through an experiential approach; revision and version control.

Sustainable architectures; design for change and expansion; software architecture design space; object oriented analysis and design; architectural styles; methodology of making architecture decisions; project organization.

Processes, threads, concurrency; synchronization mechanisms, resource management and sharing; objects and concurrency; design, architecture and testing of concurrent systems.

Data modeling, integrity constraints, principles and design of relational databases, relational algebra, SQL, query processing, transactions, concurrency control, recovery, security and data storage.

Modelling of dynamic continuous physical phenomena in both continuous and discrete time. Control theory, stability analysis and feedback controller design. Application of computer control to continuous processes. System identification.

Functional programming; lists and algebraic data types, pattern matching, parametric polymorphism, higher-order functions, reasoning about programs; lazy and strict evaluation; programming with monads; domain-specific languages.

Game concepts. Creative and expressive play. Storytelling and narratives. User interfaces for games. Gameplay. Core mechanics. Game Balancing. Software architecture of games. Level design. Genres. Physics Engines.

Mathematical foundations, the graphics pipeline, geometrical transformations, 3D visualization, clipping, illumination and shading models and the impact of graphics on society.

Oral and written presentation skills; types and structure of technical documents; software documentation for the user; formulating and presenting proposals.

Software design process. Professional responsibility. Using specifications. Documentation. Module Specification. Module interfaces. Module internal documentation. Coding styles. Portability. Software inspection. Software testing.

Linear systems, signals, filters; time and frequency domains; single input-single output systems; discrete and continuous time; sampling theorem; Fourier series; Fourier, Laplace, and z transforms; stability.

Modelling and solutions for engineering optimization problems using Linear and Integer Programming, including transportation and assignment problems, multi-objective problems and scheduling. Solution methods include primal-dual schemes (algorithms), simplex, branch and bound, and heuristics.

Software requirements gathering. Critical systems requirements gathering. Security requirements. Traceability of requirements. Verification, validation, and documentation techniques. Software requirements quality attributes. Security policies. Measures for data confidentiality. Design principles that enhance security. Access control mechanisms.

Measurements. Unit testing, slicing and debugging, integration testing, regression testing, testing strategies, test coverage.

Processes and threads, synchronization and communication; scheduling, memory management; file systems; resource protection; structure of operating systems.

Regular and Context-Free languages, Turing machines, decidability, reductions, time and space complexity classes.

Open-ended software development emphasizing concurrent system design; measurement, inspection, software metrics, software project management; testing methods.

Hard and soft real-time systems. Safety classification. Fail-safe design, hazard analysis. Discrete event systems. Modes. Requirements and design specifications. Tasks and scheduling. Clock synchronization. Data acquisition. Applications in real-time control.

Advanced topics in database systems technology and design. Topics include: query processing; query optimization; data storage; indexing; crash recovery; physical database design; introductory data mining techniques.

Physical networks, TCP/IP protocols, switching methods, network layering and components, network services. Information security, computer and network security threats, defense mechanisms, encryption.

Use of queuing models and simulation to predict computer system performance and find bottlenecks in a system. Types of models, distributions. Markov models. Modelling storage and network behaviour, locks, critical sections, concurrency. Introduction to analytical system reliability.

Parallel architectures, design and analysis of parallel algorithms; distributed-memory, shared-memory and GPU computing; communication cost, scalability; MPI, OpenMP and OpenACC; tuning parallel programs for performance.

Student teams prepare the requirements, design, documentation, and implementation of a software system taking economic, health, safety, legal, marketing factors into account. Students must demonstrate a working system and convincing test results. Software project management.

Human sensory perception, learning and cognition. Game aesthetics. Precise control and feedback mechanisms. Use of music and sounds. Critical analysis of existing interfaces. Alternate input devices.

Design of user interfaces. Principles of good interface design. Human input. Displaying complex data using graphics and virtual reality. Modes and mode awareness problem. Health issues, information overload. Special purpose graphics hardware. Interface design tools; on-line help systems.

Fundamental communications concepts: information, entropy, channel capacity, codes, data compression, adaptive channel equalizers, modulation/demodulation of signals, tracking, Kalman filtering, use of specialized signal processing hardware. Software in communication systems.

Fundamental algorithms and general duality concepts of continuous optimization. Special attention will be paid to the applicability of the algorithms, their information requirements and computational costs. Practical engineering problems will illustrate the power of continuous optimization techniques.

Computer arithmetic and roundoff error analysis. Interpolation, integration, solving systems of linear and nonlinear equations. Eigenvalues and singular value decomposition. Numerical methods for ordinary differential equations.