| Instructor | Dong Xu |
| Teaching Assistant | |
| Syllabus | This course will teach a broad set of principles and tools that will provide the mathematical, algorithmic and philosophical framework for tackling problems using Artificial Intelligence (AI) and Machine Learning (ML). AI and ML are highly interdisciplinary fields with impact in different applications, such as, biology, robotics, language, economics, and computer science. AI is the science and engineering of making intelligent machines, especially intelligent computer programs, while ML refers to the changes in systems that perform tasks associated with AI. Ethical issues in advanced AI and how to prevent learning algorithms from acquiring morally undesirable biases will be covered.
Topics may include a subset of the following: problem solving by search, heuristic (informed) search, constraint satisfaction, games, knowledge-based agents, supervised learning (e.g., regression and support vector machine), unsupervised learning (e.g., clustering), dimension reduction, learning theory, reinforcement learning, transfer learning, and adaptive control and ethical challenges of AI and ML.
Pre-requisites: Nil, but knowledge of data structures and algorithms, probability, linear algebra, and programming would be an advantage. |
| Introduction by Professor | This course will cover several topics in AI and ML. We will start with traditional AI techniques including search, probability estimation, and Bayes rule. We will then cover machine learning techniques, including unsupervised learning / reinforcement learning, and supervised learning. |
| Learning Outcomes | |
| Prior knowledge expected | Students who join this class are expected to have prior knowledge of data structures and algorithms, probability, linear algebra, and programming. |
| Compatibility | Nil |
| Topics covered | |
| Assessment | |
| Course materials | Recommended readings: - Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell and Peter Norvig
- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto
- Machine learning, by Tom Mitchell, McGraw Hill
- Machine learning: a probabilistic perspective, by Kevin Murphy, The MIT Press
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| Session dates | |
| Add/drop | 5 January, 2026 - 27 January, 2026 |
| Maximum class size | |
| Moodle course website | |