CSC 411 University of Toronto 
Machine Learning and Data Mining
An introduction to methods for automated learning of relationships on
the basis of empirical data. Classification and regression using
nearest neighbour methods, decision trees, linear models, and neural
networks. Clustering algorithms. Problems of overfitting and of
assessing accuracy. Problems with handling large databases.

CSC 321 University of Toronto 
Introduction to Neural Networks and Machine
Learning Supervised neural networks: the perceptron
learning procedure, the backpropagation learning procedure and its
applications. Elaborations of backpropagation: activation and error
functions, improving speed and generalization, Bayesian
approaches. Associative memories and optimization: Gibbs sampling,
mean field search. Representation in neural networks: distributed
representations, effects of damage, hierarchical
representations. Unsupervised neural networks: competitive learning,
Boltzmann machines, sigmoid belief nets.

CSC 108 University of Toronto 
Introduction to Computer Programming
Structure of computers; the computing environment. Programming in a
language such as Python. Program structure: elementary data
types, statements, control flow, functions, classes, objects,
methods, fields. Lists; searching, sorting and complexity. Practical
(P) sections consist of supervised work in the computing
laboratory.

CSC 180 University of Toronto 
Introduction to Computer Programming A
practical introduction to structured programming using the C
programming language with the UNIX operating system. The course will
include introductions to numerical computing and data structures and
their use. Example applications will include sorting, searching,
root finding, and numerical integration.

CSC 104 University of Toronto 
The How and Why of Computing Computer
parts and their interconnection. Software: operating systems, files,
interfaces. Hardware: storage media, memory, data representation,
I/O devices. History of computing. Problem solving with computers:
algorithms and basic programming concepts. Science and computer
science; graphics, artificial intelligence. Common computer
applications: databases, simulations. Implications for society:
computers and work, office automation, computer security. (Students
work with various applications and software, but the aim is to
discuss general concepts of computer applications, not to serve as a
tutorial for specific packages.)

SYDE 351 University of Waterloo 
Systems Models 1
Introduction to systems modelling and analysis. Graph theoretic
models and formulation of system equations. State space formulation
and solution. Time and frequency domain solutions. Application to
engineering systems.

GENE 121 University of Waterloo 
Digital Computation Introduction to
electronic digital computers, hardware and software organization,
examples of efficient numerical algorithms for basic scientific
computations. The language of instruction will be C and C++.

48520 University of Technology, Sydney 
Electronics and Circuits
The main objective of this subject is to familiarize students with common electronic devices and their applications. By the end of the subject, students should have acquired reasonable proficiency in the analysis of basic electronic circuits and be able to build and test circuits in the laboratory. Particular emphasis is placed on the practical, handson aspect of electronics to provide a solid foundation of working knowledge for all of the basic electronic devices and common electronic circuits. Laboratory work is a significant proportion of inclass delivery so as to make students proficient in circuit construction, testing, troubleshooting and to give them a sound knowledge of the use of test instruments. Another objective is to show that practical electronic applications are relevant to other engineering and technical disciplines and may often be placed within a wider social or commercial context.
Topics covered in the subject include:
 Theoretical material  basic concepts; DC circuits; AC circuits;
semiconductors; semiconductor devices; power supply; bipolar and field
effect transistor amplifiers; frequency response of amplifiers;
introduction to operational amplifiers and their applications
 Practical material  device labelling (resistor colour codes,
etc.); basics of electrical measurements, understanding of instrument
accuracy, source loading; CRO, multimeter, function generator and
other lab instruments; power supply fundamentals, floating outputs and
earth; circuit construction and systematic layout from circuit
diagrams, and deriving a circuit diagram from a physical circuit; and
fault finding.
