- Instructor: Renyu (Philip) Zhang, Associate Professor, Department of Decisions, Operations and Technology, CUHK Business School, [email protected], @911 Cheng Yu Tung Building.
- Teaching Assistant: Qinlu Hu, PhD Candidate, Department of Decisions, Operations and Technology, CUHK Business School, [email protected].
Welcome to the mono-repo for the Business Intelligence Technologies and Applications (DSME 6756BA/B) course. You may download the Syllabus of this course first. Please refer to this document to join this GitHub repo. If you have any feedback on this course, please submit it via this Survey and we will try our best to address it.
The course will be carried out in person in Wu Ho Man Yuen Building (WMY) 508 (Morning Section) and Yasumoto International Academic Park (YIA) LT6 (Afternoon Section). Please ask for my approval if you need to join us via the following Zoom links:
- Morning Section (9:30am-12:15pm): Zoom link, Meeting ID 984 6416 2488, Passcode 263862.
- Afternoon Section (2:30pm-5:15pm): Zoom link, Meeting ID 970 8075 7238, Passcode 930454.
Most of the code in this course will be in the format of Python Jupyter Notebooks. We also set up the Google CoLab environment if you prefer running the code on the cloud instead of locally. Please contact our TA Qinlu if you would like to access the Colab folder of this course.
The following schedule is tentative and subject to changes.
Session | Date | Topic | Jupyter Notebook | Slides | Assignment | Solution | Video | Video PW | Note |
---|---|---|---|---|---|---|---|---|---|
1 | 12.04 | Introduction | Python Live Code | Intro Python | PS1 | Link | ttHN.MS2 | 1 | |
2 | 12.11 | Data Handling | LinReg | Data | |||||
3 | 12.18 | Linear Models | |||||||
4 | 01.08 | BV Trade-off | |||||||
5 | 01.15 | Trees | |||||||
6 | 01.22 | Ensemble | |||||||
7 | 01.29 | Clustering | |||||||
8 | 02.06 | PCA | |||||||
9 | 02.19 | Neural Networks | |||||||
10 | 02.26 | Generative AI | |||||||
11 | 03.02 (2:00PM) | Final Exam |
We will have two projects in this course. The relevant information can be found in the table below. The schedule is tentative and subject to changes.
Project | Due | Instruction | Link | To Enter the Competition |
---|---|---|---|---|
Project 1 | 02.26 (11:59PM) | |||
Project 2 | 03.03 (11:59PM) |
We will have a final exam at 2:00PM on March 2. The exam will cover the entire course and a reference exam will be provided.
Time | Location | Format | Instruction | Sample Exam and Solution | Cheat Sheet | Real Exam and Solution |
---|---|---|---|---|---|---|
2:00pm-5:00pm, Saturday, March 2 |
We provide a lot of references for both Python programming and Analytics. For more information, please visit the links below.
If you are unfamilar with GitHub, please read this doc. However, it will suffice to know the very basics of GitHub for the need of this course.