See the syllabus for details. Machine learning has become one of the most exciting research areas in recent years. In this Python-based course, we will explore fundamental algorithms, delve into supervised and unsupervised learning methods, and examine practical applications. Along the way, you’ll build hands-on projects, develop a deep understanding of model training and evaluation, and learn how to handle real-world data challenges. Topics covered by this course include but are not limited to:
Instructor: Yao Li
Teaching Assistant: Shaleni Kovach
Grader: TBA
Class: TTH 9:30 - 10:45AM, Gardner 105
Office Hours:
| Date | Lecture | Slides | Tutorial |
|---|---|---|---|
| Jan. 09 | Welcome | ||
| Jan. 14 | Overview | ||
| Jan. 16 | Review | ||
| Jan. 21 | Linear Regression | ||
| Jan. 23 | Linear Regression Extension | ||
| Jan. 28 | Logistic Regression | ||
| Jan. 30 | Model Assessment and Selection | ||
| Feb. 04 | LDA, QDA and KNN | ||
| Feb. 06 | Support Vector Machines | ||
| Feb. 11 | Kernel SVM | ||
| Feb. 13 | Principal Component Analysis | ||
| Feb. 18 | Project Proposal Discussion | ||
| Feb. 20 | Project Proposal Discussion | ||
| Feb. 25 | Clustering Methods | ||
| Feb. 27 | Tree-based Methods | ||
| Mar. 04 | Matrix Factorization I | ||
| Mar. 06 | Matrix Factorization II | ||
| Mar. 11 | Spring Break | ||
| Mar. 13 | Spring Break | ||
| Mar. 18 | Neural Networks | ||
| Mar. 20 | Neural Networks for CV | ||
| Mar. 25 | Neural Networks for NLP | ||
| Mar. 27 | Generative Models | ||
| Apr. 01 | Reinforcement Learning | ||
| Apr. 03 | Interpretable ML | ||
| Apr. 08 | Fairness | ||
| Apr. 10 | Guest Lecture | ||
| Apr. 15 | Final Presentation | ||
| Apr. 17 | Well-being Day | ||
| Apr. 22 | Final Presentation | ||
| Apr. 24 | Final Presentation |
All homework assignments are to be submitted via Canvas. Late homework will receive a grade of 0.
| Date assigned | Instructions | Due Date (Time) |
|---|---|---|
| HW1(.ipynb) | (11:59 PM) | |
| HW2(.ipynb) | (11:59 PM) | |
| HW3(.ipynb) | (11:59 PM) | |
| HW4(.ipynb) | (11:59 PM) | |
| HW5(.ipynb) | (11:59 PM) | |
This course includes a final project in lieu of a final exam. Projects will be completed in groups of four and consist of:
Please form the final project group before Jan 22nd, and sign up using the shared spreadsheet. Please don’t modify the information of other groups.
I will meet with each group to discuss the final project topic. Project topics can be:
P1: Project Proposal (10 Points): The project proposal is limited to 2-page (excluding reference) and contains:
See latex template at link.
P2: Project Presentation (30 Points): All groups will present their final projects during the last three lectures. Every group member is expected to join the presentation. The length of the presentation depends on the number of groups (10–20min) and will be announced later.
P3: Project Paper (50 Points): Each team must submit a written project report. It is recommended to include a discussion of how your research work can be further extended. It is required to use the NeurIPS Latex style files and submit the report in PDF format. The report should be less than 8 pages without references (no minimum requirement).
P4: Peer Score (10 Points): Ten points of the final project is based on an average score measuring overall contribution as seen by you and the other members of your group. Each group member should score every person in their group on a continuous scale from 0 (Bad) to 10 (Good). Before the due date of the final paper, every member is required to submit the group scoring through the google survey link below. Your name and this information will remain private between me and you. If you fail to submit this group scoring before the deadline, 2 points penalty will be applied and I will give the other members a score of 10.
| Part | Description | Method (Location) of Submission | Due Date (Time) |
|---|---|---|---|
| P1 | Project Proposal | Canvas | Feb. 16 (11:59PM) |
| Proposal Meeting | Hanes 334 | Feb. 18 / Feb. 20 (9:30AM-10:45AM) | |
| P2 | Presentation Slides | Canvas | Before the Presentation Day |
| Final Presentation | Class | Last 3 Lectures) | |
| P3 | Final Report | Canvas | Apr. 27 (11:59PM) |
| P4 | Peer Scoring | Google Survey | Apr. 27 (11:59PM) |
Check the paper list for paper presentation opportunity.
This page was last updated on 2024-12-20 09:32:16.629319 Eastern Time.