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About W Booth School

The W Booth School of Engineering Practice and Technology within McMaster University’s Faculty of Engineering is dedicated to student-centred experiential learning through flexible, adaptable and innovative programs and teaching using state of the art resources and facilities. Our learning environment emphasizes hands-on education and transferable skills to produce engaged graduates ready to serve a diverse community and societal needs. 


Learn about the programs you can enrol in through the W Booth School of Engineering Practice and Technology.

Whether you're looking for a unique, interdisciplinary graduate program or an undergraduate degree that will help you get a job in some of the latest high-tech fields, there's something for you at the W Booth School of Engineering Practice and Technology.

Key Facts

Founded in 2005

Current Enrollment:1,500+ students (undergrad and grad)

Alumni: 2,500 and counting

92% of our alumni are employed in their field of study within six months of graduating

The late Walter G. Booth, a philanthropist, entrepreneur, and 1962 Faculty of Engineering graduate, gave generously to McMaster, the only university willing to take a chance on his non-conventional route through the post-secondary education system.

Student Spotlights

Master's program the 'most significant year' of successful policy researcher's academic journey

June 5, 2019 /  Department News

Master's program the 'most significant year' of successful policy researcher's academic journey

Gussai Sheikheldin, MEPP alumni, met with Dr. Gail Krantzberg, and shared his story.

Quality Assurance Top Priority for Graduate of W Booth School

November 15, 2018 /  Department News

Quality Assurance Top Priority for Graduate of W Booth School

Quality assurance is all about maintaining standards. It’s a comprehensive process that’s driving increased efficiencies in sectors ranging from healthcare and education to energy and manufacturing.

Pursuit of Authenticity Drives McMaster Engineering Graduate

October 30, 2018 /  Department News

Pursuit of Authenticity Drives McMaster Engineering Graduate

Live and work with passion and purpose. That’s the advice of McMaster alumnus Pouyan Safapour, Bachelor of Engineering (2009) and Master of Engineering (2011).

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Graduate Studies

Our customized graduate studies learning methodology reflects core subject matter viewed through a market-driven, experiential learning lens. 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 offer Master of Engineering Programs specializing in:

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.

Undergraduate Studies

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:

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:

Certificate Programs

Certificate in Technology: Canadian and internationally educated professionals advance their career with evening and weekend courses in technology and leadership.

Why Enrol?

Benefits from Attending the W Booth School:

  • Gain marketable technical, leadership and entrepreneurial skills
  • Access state of the art facilities and labs – including the new "Learning Factory" at W Booth
  • Connect with technical and business mentors
  • Build professional networks
  • Apply for generous bursaries and scholarships
W Booth School


Project Profiles

Students in M.Eng. Design program apply machine learning and data analytics to aquaponics farming

August 15, 2019 /  Department News

Students in M.Eng. Design program apply machine learning and data analytics to aquaponics farming

Jingpeng Zhai, a student in the Master of Engineering Design program with the W Booth School of Engineering Practice and Technology has been working in a team of three with their client to design a process to help demonstrate the viability of a indoor aquaponics farming in Canada.

How three McMaster engineering students helped build the Business Out of The Box

June 17, 2019 /  Department News

How three McMaster engineering students helped build the Business Out of The Box

Hamed Pourkeveh, Sadjad Sarhadi and Ashkan Haghshenas came to Canada from Iran last fall to begin their graduate studies in Engineering Design. Now, just eight months later, they have successfully completed a unique thesis project which is helping to make a difference for women entrepreneurs.

Eco-Pen Startup Begins to Blossom at W Booth School

November 26, 2018 /  Department News

Eco-Pen Startup Begins to Blossom at W Booth School

These two W Booth School graduate students are writing a new chapter in the story of eco-entrepreneurship in Canada.

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Artificial Intelligence Courses

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

They range from the 4th 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