Core300 LevelSecond SemesterArtificial Intelligence
CSC304
Machine Learning
Course Description
Machine Learning covers the core supervised and unsupervised learning methods, how to evaluate them honestly, and where they tend to fail. Every technique is paired with a discussion of its assumptions, so students learn to ask what a model actually knows, not just what it outputs.
Learning Outcomes
- Apply core supervised and unsupervised learning algorithms to a dataset.
- Evaluate a model's performance using appropriate metrics and validation methods.
- Diagnose overfitting and underfitting and apply appropriate remedies.
- Critically assess a model's assumptions and limitations before trusting its output.
Weekly Topics
Examines machine learning overview from a practical angle, using a case drawn from real systems to motivate the concepts.
Builds directly on the previous week to extend linear regression, with an emphasis on where the earlier techniques stop working.
Combines a short lecture on classification fundamentals with an in-class exercise students carry into the week's assignment.
Focuses on common mistakes and misconceptions around model evaluation & validation, using student work from the previous assignment as material.
Introduces decision trees & ensembles and immediately puts it to use in a small design or implementation task.
Situates support vector machines within the broader arc of the course, showing how it connects to what comes next.
Uses a guest dataset or scenario to explore mid-semester review in a setting closer to professional practice.
Introduces clustering through short lectures and worked examples, building the vocabulary the rest of the course relies on.
Works through dimensionality reduction in a lab-driven session, with guided exercises students complete and discuss in small groups.
Covers introduction to neural networks in depth, connecting the underlying theory to a concrete example the class builds together.
Examines overfitting, regularization & bias from a practical angle, using a case drawn from real systems to motivate the concepts.
Builds directly on the previous week to extend applied project, with an emphasis on where the earlier techniques stop working.
Combines a short lecture on revision with an in-class exercise students carry into the week's assignment.
Assessment
Assignments — 20%
Practical Work — 30%
Mid-Semester — 15%
Final Examination — 35%
Prerequisite Map
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