 |
| PRE-REQUISITE | UFUG 2601 OR UFUG 2602 |
|---|---|
| DESCRIPTION | The objective of this course is to present an overview of the principles and practices of AI and to address complex real-world problems. Through introduction of AI tools and techniques, the course helps students develop a basic understanding of problem solving, search, theorem proving, knowledge representation, reasoning and planning methods of AI; and develop practical applications in vision, language, and so on. Topics include foundations (search, knowledge representation, machine learning and natural language understanding) and applications (data mining, decision support systems, adaptive web sites, web log analysis). |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6045) | 14-JUL-2025 - 05-AUG-2025 TuWeThFr 03:00PM - 05:50PM | Rm 233, W1 | LIU, Li | 40 | 39 | 1 | 0 | > Add/Drop Deadline: 16 July 2025 |
| PRE-REQUISITE | UFUG 1103 OR UFUG 1106 |
|---|---|
| DESCRIPTION | This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative, Taylor series, Multivariate Calculus), and Probability Theory (Distributions, Statistics of Random Variables, Bayes’ theorem). With these mathematical concepts, some basic principles of numerical optimization and typical AI algorithms (Gradient Descent, Maximum-likelihood, Regression, Least Square Estimation, Spectral Clustering, Matrix Decomposition, etc.) will also be introduced as examples to better relate math to AI. The approach of this course is specifically AI application oriented, aiming to help students to quickly establish a fundamental mathematical knowledge structure for AI studies. Through this course, students will acquire the fundamental mathematical concepts required for AI, understand the connections between AI and mathematics, and get prepared to learn the mathematical principles, formulas, inductions, and relevant proofs for advanced AI algorithms. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6043) | 16-JUN-2025 - 09-JUL-2025 MoWeTh 06:00PM - 09:20PM | Rm 102, W1 | RIKOS, APOSTOLOS | 40 | 35 | 5 | 0 | > Add/Drop Deadline: 18 June 2025 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers popular topics in computer vision, which includes high-level tasks like image classification, object detection, image segmentation, and low-level tasks like image generation, image enhancement, image-to-image translation, etc. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6003) | 16-JUN-2025 - 07-JUL-2025 MoTuWeFr 01:30PM - 04:20PM | Rm 201, W1 | CHEN, Yingcong | 20 | 19 | 1 | 0 | > Add/Drop Deadline: 17 June 2025 > Extended Drop Deadline: 20 June 2025 |
| VECTOR | [1-3 credit(s)] |
|---|---|
| DESCRIPTION | An independent research project carried out under the supervision of a faculty member. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6067) | TBA | TBA | TBA | 20 | 0 | 20 | 0 |
| VECTOR | [1credit] |
|---|---|
| DESCRIPTION | An independent research project carried out under the supervision of a faculty member. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6068) | TBA | TBA | TBA | 20 | 0 | 20 | 0 |