 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course aims to provide students with an overview of Artificial Intelligence (AI) principles and techniques. Key topics include machine learning, search, game theories, Markov decision process, constraint satisfaction problems, Bayesian networks, etc. Through this course, students will learn and practice the foundational principles, techniques and tools to tackle new AI problems. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6053) | Fr 09:00AM - 11:50AM | Rm 201, E1 | LIANG, Junwei | 30 | 22 | 8 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course aims to provide students with key principles and algorithms to build modern autonomous AI systems. Key topics include machine perception, planning and decision-making algorithms. Through this course, students will learn and practice the foundational principles, techniques, and tools to build new autonomous AI systems. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6052) | Fr 01:30PM - 04:20PM | Rm 201, E1 | LIANG, Junwei | 30 | 24 | 6 | 0 |