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

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.

Lecturer:
Yu LI (liyuATcse.cuhk.edu.hk), SHB-106. Office hour: 3pm-5pm, Friday

TA:

- Licheng ZONG (lczongATlink.cuhk.edu.hk), SHB-1026. Office hour: 2:30pm-4:30pm Tuesday
- Liang HONG (liang.hongATlink.cuhk.edu.hk), SHB-116. Office hour: 10:30am-12:30pm Tuesday

**Monday**: **2:30pm-4:15pm**, **ERB-703**.

**Thursday**: **2:30pm-3:15pm**, **ERB-804**.

Thursday: **3:30pm-4:15pm**, **ERB-804**. Tutorial

Thursday: **5:30pm-6:15pm**, **ERB-406**. ESTR-4140

Mainly onsite. Slides will be available the day before the lecture day. We will also provide the Zoom session.

**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.

**Homework (42%)**: One non-grading assignment (6%, set up the environment) and three grading homework (12%+12%+12%).**Scribing (6%)**: Grading scribing. Summarize one of the lectures. Submit it within one week after the course. Each student should do at least one lecture. You can sign for at most two, for additional 1%.**In-class quiz (5%)**: One in-class quiz. The questions will be simple. Mainly for checking the participation. The exact date are in the below schedule.**Project (47%)**: A grading project. You should submit a proposal (6%), a mid-term report (7%), a final report (17%) and give a presentation (17%).

**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. Register here:

The quiz is open-booked.

Half of them will be fixed-answer questions while half of them will be **Kaggle competition**. The last Kaggle competition is optional. If you participate in that one, you final score of the Kaggle part will be the highest two out of the three.

- You need to compete with your
**classmates**. -
The TA will set up a baseline using
**linear regression**and**simple deep learning**. If you are better than the baseline, you can be above 60%. If you are better than the deep learning baseline, you can be above 80%. - The final score of each student will be based on the ranking. The first one gets 100%, and the last one above the deep learning baseline gets 80%. Scores will be interpolated linearly.

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 27th Jan**. After that, the Google sheet will be closed. Here are some good scribing examples from another course.

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 (6%), a mid-term report (7%), a final report (17%) and give a presentation (17%). **Both the lecturer (90%) and the students (10%) will be the markers**.

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.

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.

Lec | Date | Location | Topic | Slides/Video | Notes | Reading | Important dates (All due at 11:59 pm) |
---|---|---|---|---|---|---|---|

1 | Jan 9 (Mon) | ERB-703 | Introduction | Lec-1 | |||

2 | Jan 12 (Thu) | ERB-804 | ML review | Lec-2 | Data mining book, D2L, Colab, Kaggle | A0 posted | |

3 | Jan 16 (Mon) | ERB-703 | LR/NN | Lec-3 | D2L, Universal approximation theorem | ||

4 | Jan 19 (Thu) | ERB-804 | Backpropagation | Lec-4 | D2L, Universal approximation theorem, Chain rule, Subgradient | ||

- | Jan 23 (Mon) | - | - | - | - | - | Lunar New Year Vacation |

- | Jan 26 (Thu) | - | - | - | - | - | Lunar New Year Vacation, Scribing preference, 27 Jan |

5 | Jan 30 (Mon) | ERB-703 | CNN | A0 due, A1 posted |
|||

6 | Feb 2 (Thu) | ERB-804 | Overfitting | ||||

7 | Feb 6 (Mon) | ERB-703 | CNN++ | ||||

8 | Feb 9 (Thu) | ERB-804 | CNN++ | ||||

9 | Feb 13 (Mon) | ERB-703 | Optimization | ||||

10 | Feb 16 (Thu) | ERB-804 | Optimization | A1-paper due |
|||

11 | Feb 20 (Mon) | ERB-703 | Loss function | Project proposal |
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12 | Feb 23 (Thu) | ERB-804 | Text processing | A1-Kaggle due, A2 posted |
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13 | Feb 27 (Mon) | ERB-703 | RNN | ||||

14 | Mar 2 (Thu) | ERB-804 | RNN++ | ||||

- | Mar 6 (Mon) | - | - | - | - | - | Reading Week |

- | Mar 9 (Thu) | - | - | - | - | - | Reading Week |

15 | Mar 13 (Mon) | ERB-703 | RNN++ | A2-paper due, A3 posted |
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16 | Mar 16 (Thu) | ERB-804 | Attention | Project M-report |
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17 | Mar 20 (Mon) | ERB-703 | BERT | A2-Kaggle due |
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18 | Mar 23 (Thu) | ERB-804 | NLP | ||||

19 | Mar 27 (Mon) | ERB-703 | Graph | ||||

20 | Mar 30 (Thu) | ERB-804 | GNN | A3-paper due |
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21 | Apr 3 (Mon) | ERB-703 | GAN | A3-Kaggle due |
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22 | Apr 6 (Thu) | ERB-804 | Generative | ||||

- | Apr 10 (Mon) | - | - | - | - | - | Easter Monday |

23 | Apr 13 (Thu) | ERB-804 | Course summary | Participation Quiz |
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24 | Apr 17 (Mon) | ERB-703 | Project pres | ||||

25 | Apr 20 (Thu) | ERB-804 | Project pres | Project report |

- Assignment 1: Image
- Assignment 2: Sequence
- Assignment 3: Graph