ENGTECH 1PR3

Object-Oriented Programming

Undergraduate 3 unit(s) Project-based course covering computer programming. Pointers, structured data, object-oriented concepts, inheritance, polymorphism, abstract class and overloading functions. Two lectures, one lab (two hours); second term Prerequisite(s): ENGTECH 1CP3 and registration in Automation Engineering Technology or Automotive and Vehicle Engineering Technology

SFWRTECH 4NN3

Neural Networks and Deep Learning

Undergraduate 3 unit(s) Neural network architectures and training, parallel implementations and implementations using accelerated hardware, convolutional neural networks, recurrent neural networks and deep learning. Three lectures, one term; completely virtual Prerequisite(s): Registration in Level IV Software Engineering Technology This course is graded on a pass/fail basis.

SFWRTECH 4DM3

Data Mining

Undergraduate 3 unit(s) Classification, association, prediction and clustering of data. Decision trees. Bayesian probability. Supervised and unsupervised learning. Three lectures, one lab; one term; completely virtual Prerequisite(s): SFWRTECH 4DA3 and registration in Software Engineering Technology This course is graded on a pass/fail basis.

SFWRTECH 4DA3

Data Analytics and Big Data

Undergraduate 3 unit(s) Linear approaches to regression and classification - selection and shrinkage methods, linear discriminant approaches, big data system implementations. Three lectures, one term; completely virtual Prerequisite(s): ENGTECH 3ST3 or 3ES3, SFWRTECH 4MA3, and registration in Biotechnology or Software Engineering Technology This course is graded on a pass/fail basis.

SFWRTECH 4CC3

Parallel Programming

Undergraduate 3 unit(s) Models of parallel computation. Performance measurement. Shared memory and synchronization. Parallel data structures, searching and sorting. Scheduling and work distribution. Three lectures, one term; completely virtual Prerequisite(s): Registration in Level IV Software Engineering Technology This course is graded on a pass/fail basis.

SEP 6DM3

Data Mining

Undergraduate Classification, association, prediction and clustering of data through the use of machine learning algorithms - from introductory approaches to recent advances. Evaluation metrics. Meta learning. Deep Learning.

SEP 6DA3

Data Analytics and Big Data

Undergraduate Statistical Data Analysis, Linear Regression using OLS, MLE and FLD, Linear Classification using the perceptron, neural networks and the SVM, Big Data Architectures.

CHEM ENG/SEP 787

Machine Learning : Classification Models

Undergraduate Discriminant analysis and other linear classification. Linear Support Vector Machine. Nonlinear classification models. Classification Trees and Rule based models. Remedies for severe class imbalance.

CHEM ENG/SEP 786

Artificial Intelligence and Machine Learning Fundamentals

Undergraduate Solving Problems using AI: Searching, optimization, online search agents. Constraint satisfaction. Knowledge, Reasoning and Planning: Logic and Inference, Planning and Acting, Knowledge Representation. Knowledge and Reasoning with Uncertainty. Machine learning problems, training and testing, overfitting. Modelling strategies: data preprocessing, overfitting and model tuning. Measuring predictor importance. Factors that Can Affect Model Performance. Feature selection. Measuring performance of classification models.