Instructor:
-
Prof. Qi DOU
Dept. of Computer Science & Engineering
Email: qidou@cuhk.edu.hk
Office: Room 911, 9/F, SHB, CUHK
Consultation hours: Monday 3pm-5pm -
Zirui Wang (Coordinator, ESTR 3108)
Dept. of Computer Science & Engineering
Email: wangzr26@cse.cuhk.edu.hk
-
Jihai Zhang
Dept. of Computer Science & Engineering
Email: jhzhang24@cse.cuhk.edu.hk
-
Wenxin Fan
Dept. of Computer Science & Engineering
Email: wxfan25@cse.cuhk.edu.hk
-
Siyuan Huang
Dept. of Computer Science & Engineering
Email: syhuang26@cse.cuhk.edu.hk
-
Haoyuan Wu
Dept. of Computer Science & Engineering
Email: hywu24@cse.cuhk.edu.hk
TAs:
Useful links
News
- Welcome to CSCI3230 2025-2026 (Term 1)!
- First lecture will start on 8 Sep 14:30 - 16:15 Science Centre L1!
Course information
This course aims to widely introduce the basic knowledge, classic algorithms, neural networks, searching, cutting-edge technical concepts and models in artificial intelligence. Topics to be covered include introduction to artificial intelligence, basics of key concepts, linear regression, logistic regression, clustering, SVM, PCA, neural network basics and deep learning, advanced AI models for big data, and latest applications of AI in multiple domains. On the theoretical side, students are expected to learn the relevant knowledge and mathematics in artificial intelligence from lectures and tutorials. On the practical side, students are given written assignments and programming assignments to get hands-on experience to apply the learned knowledge to solve problems. The field of artificial intelligence is advancing rapidly. Through this course, students will not only learn solid fundamentals of AI, but also know the latest news about AI on the globe.
Class time
Lectures:
Tuesday 14:30pm - 16:15pm Science Centre L1
Thursday (ESTR 3108) 09:30am - 10:15am LSB LT1
Thursday 10:30am - 11:15am Science Centre L1
Tutorials:
(T01) Wednesday 13:30pm - 14:15pm Science Centre L5
(T02) Wednesday 17:30pm - 18:15pm Science Centre L5
(T03) Thursday 9:30pm - 10:15pm ERB 803
(T04) Thursday 10:30pm - 11:15pm Science Centre L3
Piazza
For discussions and questions: go to here
Assessments:
CSCI3230:
Assignments:
60%
Final examination:
35%
Class participation:
5%
ESTR3108:
Assignments:
40%
Article reading:
20%
Final examination:
35%
Class participation:
5%
Tentative class schedule
| Week | Time | Topic | Instructor | TA | ||||
|---|---|---|---|---|---|---|---|---|
| 1 | Sep 8,10 | Introduction | Prof. Dou | - | ||||
| 2 | Sep 15,18 | Linear Regression | Prof. Dou | - | ||||
| 3 | Sep 22,24 | Logistic Regression | Prof. Dou | - | ||||
| 4 | Sep 29 | Support Vector Machine | Prof. Dou | - | ||||
| 5 | Oct 6,8 | Clustering Algorithms | Prof. Dou | - | ||||
| 6 | Oct 13,15 | Neural Network Basics | Prof. Dou | - | ||||
| 7 | Oct 20,22 | Convolutional Neural Networks | Prof. Dou | - | ||||
| 8 | Oct 27,29 | Transformers | Prof. Dou | - | ||||
| 9 | Nov 3,5 | Uninformed Search | Prof. Dou | - | ||||
| 10 | Nov 10 | Informed Search | Prof. Dou | - | ||||
| 11 | Nov 17,19 | AI Applications in Healthcare | Prof. Dou | - | ||||
| 12 | Nov 24,26 | AI Applications in HCI | Prof. Dou | - | ||||
| 13 | Dec 1,3 | AI Applications in Robotics | Prof. Dou | - | ||||
Useful materials and links
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville.
- Artificial Intelligence: A Modern Approach, 4th Global ed. by Stuart Russell, Peter Norvig
- Google AI Blog
- MIT Technology Review, Artificial Intelligence.