ENGG*6600: Advanced Machine Learning School of Engineering, University of Guelph Summer 2022 Handout (Course Outline). 1st Meeting (Introduction, PPT). 1st Meeting (Introduction, PDF). Instructor:

Lab Coordinator:

Class Times (Online using Webex):

Tuesday: 14:30 PM - 15:50 PM .. either in MCKN 224 or via WebEx

Thursday: 14:30 PM - 15:50 PM .. either in MCKN 224 or via WebEx

Course Description:

This course places special emphasis on the area of representation learning and deep learning.
However, this course provides a broad overview of the field of machine learning.
Students are encouraged to explore practical applications of these techniques across a wide variety of 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 25%

Project: Report/Demo 30%

Paper Review: Presentation 10%

Final Exam: Closed Book 35%

This page is maintained by Shawki Areibi, sareibi@uoguelph.ca
Last modified Jan. 2023