Our customized graduate studies learning methodology reflects core subject matter viewed through a market-driven, experiential learning lens. The curriculum is structured so that academics converge with learning by practice in real time. Creativity and innovation are nurtured throughout this educational experience. Program graduates will have a set of technical and professional skills that enable an enhanced range of career choices in tomorrow’s world.

We off Master of Engineering Programs specializing in:

• Engineering Design
• Engineering and Public Policy
• Engineering/Technology Entrepreneurship and Innovation
• Manufacturing Engineering
• Systems and Technology

Featured Initiatives:

• Practitioner’s Forum
• Innovation Studio
  • An Initiative of the W Booth School of Engineering Practice and Technology where students learn how to pursue value creation through the practice of thought leadership, interdisciplinary initiatives and community building.

Combined Degree/Diploma

High school or post-secondary transfer students earn a degree, diploma, and 12 months of work experience in 4.5 years of study.

Program Streams:

• Automotive & Vehicle Engineering Technology
• Biotechnology
• Automation Engineering Technology

Degree Completion Program

College and University graduates advance directly to level 3 of a degree.  Flexible evening and weekend courses make it possible for technologists and internationally educated professionals to gain Canadian credentials while working.

Program Streams:

• Civil Engineering Infrastructure Technology
• Power and Energy Engineering Technology
• Manufacturing Engineering Technology
• Software Engineering Technology

Certificate Programs

• Certificates of Completion (new program!)
• Certificate in Technology
• Technology Leadership Certificate

W Booth School offers a series of courses in artificial intelligence.

They range from the 4^th year undergraduate courses (designated as course code 4′ hundreds and 6′ hundreds) to graduate level courses.

Currently offered courses are:

SEP 786 Artificial Intelligence and Machine Learning Fundamentals

• 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.

SEP 787  Machine Learning : Classification Models

• Discriminant analysis and other linear classification: Logistic regression, Linear discriminant analysis, Partial least squares discriminant analysis, Penalized models, Nearest shrunken centroids
• Linear Support Vector Machine: Empirical vs structural risk minimization, Soft margin classifier
• Nonlinear classification models: Nonlinear discriminant analysis, Flexible discriminant analysis, Support vector machines, K-nearest neighbours, Naïve Bayes
• Classification Trees and Rule based modes: Basic classification trees, Rule-based models, Bagged trees, Random forests, Boosting
• Remedies for severe class imbalance: The effect of class imbalance, Model tuning, Alternate cutoffs, Adjusting prior probabilities, Unequal case weights.

SEP 788 Neural Networks and Development Tools

• Introduction to AI landscape.
• Tools for building a machine learning project (Python crash course).
• Neural Networks Mathematical background.
• Hyperparameters, Generalization, Training process.
• Using Python scikit-learn.
• Project 1 – build and train a neural network using python scikit-learn
• Neural networks vs deep learning.
• TensorFlow
• Applying TensorFlow for Machine learning,
• Project 2- use TensorFlow/Keras for defining and training a deep learning network (use case).
• Other DNN frameworks and hardware.
• Project 3- More complex deep learning challenge (using Google cloud or Azure) .

SEP 789 Deep Learning and Its Applications

• Image processing using machine learning.
• Convolutional Neural Network (CNN) Architecture and training.
• Image recognition application.
• Transfer learning, Image classification, Image segmentation, Image labelling, Clinical imaging.
• Project 1 – build and train a CNN network (image processing).
• Recurrent Neural Network (RNN) Architecture; RNN Applications (Time series, Stock market).
• Deep learning application in Natural language processing.
• Smart manufacturing: Comparison between deep learning and traditional machine learning, Product quality inspection, Fault assessment, Defect prognosis.
• Project 2 – build and train a RNN network.
• Reinforcement learning: Architecture; Application in Robotics, Application in display advertising.
• Advanced topics: Autoencoders, Deep Residual Networks, Generative Models (GANs).
• More Applications: Digital & Smart Systems, Energy, Transportation, Micro-Nano Systems, Advanced Manufacturing
• Project 3 – select one project out of 3-4 themes with data sets to apply deep learning with minimal guidance.

SEP 767 Multivariate statistical methods for Big Data analysis and process improvement

• Course overview and introduction
• Principal Component Analysis (PCA)
• Applications of PCA
  • Monitoring processes
  • Data visualization
• Projection to Latent Structures (PLS)
• Applications of Latent Variable Methods
  • Soft Sensors
  • Advanced pre-processing
• Working with images
• Classification
• Dealing with batch data
• Multiblock data analysis