ENGG*6600: Machine Learning with Applications School of Engineering, University of Guelph Fall 2025 Handout (Course Outline). 1st Meeting (Introduction, PPT). 1st Meeting (Introduction, PDF). Instructor:

Lab Coordinator:

Class Times (MCKN 305):

Wed: 14:30 AM - 16:00 PM .. in RICH 2531

Fri: 14:30 AM - 16:00 PM .. in RICH 2531

Course Description:

Machine Learning is a subfield of Artificial Intelligence that focuses on studying techniques and algorithms that enable computer systems to learn from experience.
This course serves as a foundation for further academic or industry work in the age of big data.
This course places special emphasis on the area of data preparation (preprocessing, postprocessing), algorithm comparison and evaluation, complexity analysis of algorithm.
The course covers both supervised and unsupervised learning algorithms along with advanced ensemble based machine learning techniques.
It also seeks to clarify and explain the relationship between traditional machine learning and deep learning.
Students are encouraged to explore practical applications of these techniques across a wide variety of engineering domains.

Course Objective:

1. Understand the basic concept of Machine Learning with its different flavours of Supervised Learning and Unsupervised Learning.

2. Teach students about the theory and implementation of Machine Learning Algorithms.

3. Familiarize the students with advantages/disadvantages and limitations of Machine Learning and the applications that can benefit from it.

4. Acquaint students with state of the art Machine Learning tools for implementing applications such as Scikit-Learn, Keras and TesorFlow, e.t.c

Reference:

1. ``"Introduction to Machine Learning'', by Ethern Alpaydin, MIT Press, 2020.

2. ``Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow'', by Aurelien Geron, O'Reilly Media Inc, 2019.

3. ``A Course in Machine Learning'', by Hal Daume III, 2017, freely available.

4. ``Deep Learning", by Ian Goodfellow, Yoshua Bengio, and Aaron Couville, MIT Press, 2016

5. ``Machine Learning: A Probabilistic Perspective" by Kevin Murphy, NIT Press, 2012.

Evaluation:

Assignments: Assignments 20%

Project: Report/Demo 30%

Final Exam: Closed Book 50%

This page is maintained by Shawki Areibi, sareibi@uoguelph.ca
Last modified August. 2025