AIST1000: Introduction to Artificial Intelligence and Machine Learning-Fall 2022

[Pre-course survey, Piazza, Scribing preference, Logistics, Course schedule and materials]

Course description

This course covers the basic concepts, problems, approaches and applications of artificial intelligence and machine learning. It provides an introduction to various topics in AI systems and technologies, e.g., an overview of AI, machine learning theory and methods, ML in data science, neural networks and deep learning, hardware and software technologies for AI systems, natural language processing, computer vision, AI in games and sports, biomedical intelligence, intelligent manufacturing and robotics, ethical and legal issues with AI, etc. It discusses the applications of engineering principles to selected AI and ML problems. It also explores the future possibilities and challenges of AI.

Teaching team

Lecturer: Yu LI (, SHB-106. Office hour: 3pm-5pm, Friday

Time and location

Tuesday: 10:30am-12:00pm, LPN-LT.


Onsite. This is a seminar course, for which we have a number of speakers. For some lectures, the slides may not be available considering the preference of the speaker.



Blackboard is the main software to manage the course, and grading will be through blackboard. We will use Piazza (AIST1000) for discussion. You can ask questions through Piazza, even anonymously. For a personal matter, please use the private post to the instructor and the TA. You are also very welcomed to send emails to the instructor and TAs.


Bonus (up to 2.5%): One additional scribing: 1%. Pre-course survey + Post-lecture survey: 0.3% for each, and the maximum is 1.5%. I do encourage you to complete all of them so that to let me know your feedback and adjust the course accordingly. Send your names to the TA.


Please sign Scribing preference. We should have at least one student for each lecture. We may adjust the assignment if necessary. Notice that your note and scribing will be posted online, for others reference. You can choose to remove your name or not. Deadline for signing the scribing: 11:59 pm on 19th Sep. After that, the Google sheet will be closed.


You should do the project individually. You should submit a proposal (10%), a final report (20%) and give a presentation (20%). Both the lecturer (80%) and the students (20%) will be the markers.

Late days

Each student will have 6 late days to turn in scribing, project proposal, and project final report. A maximum of 2 late days can be used for each assignment. Grades will be deducted by 25% for each additional late day.

Post-lecture survey

Deadline for each survey: 11:59pm on the day before the next lecture. We do this because I could have time to answer the questions you mentioned in the survey. Please fill 1 in the Google sheet: Survey results, once you have finished one survey. Usually, we will trust the 1s you fill in the Google sheet. But we will check the things in detail if the number of survey forms we received and the number of 1s on the Google sheet is not consistent.

Course schedule and materials

Lec Date Location Topic Lecturer Contact Slides/Video Notes Important dates (All due at 11:59 pm)
1 Sep-06 LPN LT Course overview and logistics Prof. Yu Li (CSE) Lec-1, AIST-2022    
2 Sep-13 LPN LT AI project overview, AI for drug discovery Prof. Yu Li (CSE) Lec-2, Reading material    
3 Sep-20 LPN LT Prof King’s journey in AI Prof. Irwin King (CSE) Lec-3 CSE department chairman  
4 Sep-27 LPN LT Biomedical intelligence Prof. Raymond Tong (BME) Lec-4 BME department chairman  
5 Oct-11 LPN LT Intelligent multimedia processing: CV Prof. Hongsheng Li (EE) Lec-5    
6 Oct-18 LPN LT Intelligent robotics Prof. Qi Dou (CSE) Lec-6   Proposal
7 Oct-25 LPN LT Intelligent multimedia processing: Speech and language Prof Tan Lee (EE) Lec-7 Associate Dean for Education in the Faculty of Engineering, CUHK Vice-Chancellor’s Exemplary Teaching Award, Faculty of Engineering’s Exemplary Teaching Awards*12  
8 Nov-01 LPN LT Optimization for machine learning Prof. Anthony So (SEEM) Lec-8 UGC Teaching Award-2022, CUHK Vice-Chancellor’s Exemplary Teaching Award-2013  
9 Nov-08 LPN LT Multi-modality AI system Prof. Liwei Wang (CSE) Lec-9    
10 Nov-15 LPN LT Large-scale machine learning and hardware Prof. Bei Yu (CSE) Lec-10    
11 Nov-22 LPN LT Project presentation Students   Lec-11   Quiz
12 Nov-29 LPN LT Project presentation Students       Report