AIST4010: Foundation of applied deep learning-Spring 2022

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

Course description

This course covers how to use deep learning techniques to resolve real-life computational problems, handling different kinds of data. We start the course by introducing the problem-solving paradigm with deep learning: data preparation, building the model, training the model, model evaluation, and hyper-parameter searching. Then, we fill in the details in the paradigm. Regarding the deep learning models, we will go from the simplest linear regression model, towards the relatively complicated models. To handle various data types, that is, the structured data, images, text, sequences, signals, and graphs, in our daily life, we would cover CNN/ResNet, RNN/LSTM, Attention, and GNN models. In addition to the above paradigm, we will also cover the commonly used techniques to handle overfitting. We would briefly go through the generative models, VAE, and GAN, at the end of this course.

Teaching team

Lecturer: Yu LI (, SHB-106. Office hour: 3pm-5pm, Friday
TA: Licheng ZONG (, SHB-1026. Office hour: 2:30pm-4:30pm, Tuesday

Time and location

Monday: 2:30pm-4pm, ERB-712.
Thursday: 2:30pm-3:15pm, YIA-503.
Thursday: 3:30pm-4:15pm, YIA-503. Tutorial


Mixed. Slides will be available the day before the lecture day. Video recordings will be available after the lecture.



Blackboard is the main software to manage the course, and grading will be through blackboard. We will use Piazza (AIST4010) 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 3%): One bonus question in Midterm (1%). One additional scribing: 0.5%. 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.

Open-book quiz and exam policy

All exams and quiz are open-book. You are allowed to take any paper-based materials. However, no phone or computer is allowed. Other communication tools are also not allowed. Discussion is not allowed.


Half of them will be fixed-answer questions while Half of them will be Kaggle competition.


Python or any other you are familiar with. For python, we suggest you to use Colab.


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 17th Jan. After that, the Google sheet will be closed.


You can choose to do the project individually or team-up. However, for the team-up project, we will have higher requirement and the project should target at publication. Moreover, the contribution of each student and the workload split should be defined clearly at the beginning of the project. Please discuss with me if you want to do serious team-up project. You should submit a proposal (5%), a mid-term report (6%), a final report (16%) and give a presentation (16%). Both the lecturer (80%) and the students (20%) will be the markers.

Late days

Each student will have 6 late days to turn in assignments, which can be used on A1, A2, A3, project proposal, and project M-report. They cannot be used on the project final report and the scribing note. 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 Slides/Video Notes Reading Important dates (All due at 11:59 pm)
1 Jan 10 (Mon) ERB-712 Introduction Lec-1, YouTube, Bilibili      
2 Jan 13 (Thu) YIA-503 ML review Lec-2, Lec video unavailable due to tech issues, YouTube, Bilibili   Data mining book, D2L, Colab, Kaggle A0 posted
3 Jan 17 (Mon) ERB-712 LR/NN Lec-3, YouTube, Bilibili   D2L, Optimization, Hessian matrix, Positive definite  
4 Jan 20 (Thu) YIA-503 Backpropagation Lec-4, YouTube, Bilibili   D2L, Universal approximation theorem, Chain rule, Subgradient A0 due, A1 posted
5 Jan 24 (Mon) ERB-712 CNN Face-to-face?      
6 Jan 27 (Thu) YIA-503 Overfitting        
- Jan 31 (Mon) - - - - - Lunar New Year Vacation
- Feb 3 (Thu) - - - - - Lunar New Year Vacation
7 Feb 7 (Mon) ERB-712 Advanced CNN        
8 Feb 10 (Thu) YIA-503 Text & Embedding       Project proposal
9 Feb 14 (Mon) ERB-712 RNN/LSTM       A1 due, A2 posted
10 Feb 17 (Thu) YIA-503 Attention        
11 Feb 21 (Mon) ERB-712 BERT        
12 Feb 24 (Thu) YIA-503 - - - - 2:30pm-4pm, Mid-term
13 Feb 28 (Mon) ERB-712 Foundation model        
14 Mar 3 (Thu) YIA-503 Graph & embedding        
15 Mar 7 (Mon) ERB-712 GNN       A2 due, A3 posted
16 Mar 10 (Thu) YIA-503 GAT        
17 Mar 14 (Mon) ERB-712 VAE       Project M-report
18 Mar 17 (Thu) YIA-503 GAN        
19 Mar 21 (Mon) ERB-712 Self-supervised learning        
20 Mar 24 (Thu) YIA-503 Model interpretability        
21 Mar 28 (Mon) ERB-712 Privacy & distributed learning       A3 due
22 Mar 31 (Thu) YIA-503 How to pre & How to write        
- Apr 4 (Mon) - - - - - Reading Week
- Apr 7 (Thu) - - - - - Reading Week
23 Apr 11 (Mon) ERB-712 Course summary       ParticipationQ
24 Apr 14 (Thu) YIA-503 Project pres        
- Apr 18 (Mon) - - - - - Easter Monday
25 Apr 21 (Thu) YIA-503 Project pres       Project report

Assignments (To be refined)