CSCI 3151

Foundations of Machine Learn.

Lec 01 selected

This course aims to introduce the machine learning, deep networks, natural language processing, the process and practice of data science, data mining and data warehousing. The following topics will be introduced: linear regression, optimization by gradient descent, support vector machines, decision tree classifiers, naive Bayes classifiers, multilayer neural networks, clustering, evaluation of classification and clustering algorithms, cross-validation, avoiding overfitting. Students will use a standard machine learning library to apply algorithms to various datasets.

Sections

Lecture

From the syllabus

From the Winter 2026 syllabus by Frank Rudzicz. Open the PDF

Grading

10%Participation and discussionTeam-based; late notebooks capped at 33%
25%Weekly quizzesLowest quiz mark discarded
25%Practical assignments (5)Weighted 3%, 4%, 5%, 6%, and 7%
15%Midterm exam
25%Final examCumulative; written and practical

Textbooks

  • Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville (optional)
  • Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong (optional)
  • Probabilistic Machine Learning, Kevin Murphy (optional)

Course rules

Late notebooks
In-class module notebooks may be submitted after class, but the maximum score is 33%.
Missed work
The Student Declaration of Absence policy applies.
AI tools
AI tools may support learning, but some assessments prohibit them; acknowledge tools where allowed.
Remote proctoring
The instructor may require remote proctoring; students unable to meet system requirements should contact the instructor.
Citations
Failure to properly attribute sources may lose points or lead to an academic integrity allegation.

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