COMP219: Advanced Artificial Intelligence
Every week, I will try to arrange a one-hour office time. However, please send an email to me to ensure my availability before coming.
- To equip students with the knowledge about basic algorithms that have been used to enable the AI agents to conduct the perception, inference, and planning tasks;
- To equip students with the knowledge about machine learning algorithms;
- To provide experience in applying basic AI algorithms to solve problems;
- To provide experience in applying machine learning algorithms to practical problems
- Ability to explain in detail how the techniques in the perceive-inference-action loop work
- Ability to choose, compare, and apply suitable basic learning algorithms to simple applications
- Ability to explain how deep neural networks are constructed and trained, and apply deep neural networks to work with large scale datasets
- Understand reinforcement learning, and is able to develop deep reinforcement learning algorithms for suitable applications
There will be 26-30 lectures. The lecture notes will appear here one week ahead of the lectures.
There will be two project assignments, which will be disclosed in due course. Each coursework assignment takes 10% of the final mark.
For general game design and implementation, the following books are recommended:
- Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson; third edition (18 May 2016)
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press. 2017