About the Course

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:

  1. Machine Learning Overview
  2. Linear Models (Linear Regression; Logistic Regression; SVM)
  3. Non-linear Models (Tree-based Methods; Kernel SVM; Neural Networks)
  4. Clustering Methods
  5. Matrix Factorization
  6. Advanced topics

Course Material

Date Lecture Slides Tutorial
Jan. 07 Logistics and Overview
Jan. 12 Overview Tutorial 1
Jan. 14 Linear Regression I
Jan. 19 Linear Regression II
Jan. 21 Linear Regression Extension Tutorial 2
Jan. 26 Binary Classification I
Jan. 28 Project Proposal Discussion
Feb. 02 Binary Classification II
Feb. 04 Support Vector Machines I
Feb. 09 Support Vector Machines II
Feb. 11 Support Vector Machines III Tutorial 3
Feb. 16 Tree-based Methods I
Feb. 18 Tree-based Methods II
Feb. 23 Tree-based Methods III Tutorial 4
Feb. 25 Clustering Methods I
Mar. 02 Midterm Review
Mar. 04 Midterm Review
Mar. 09 Spring Break — No Class
Mar. 11 Spring Break — No Class
Mar. 16 Clustering Methods II
Mar. 18 Clustering Methods III Tutorial 5
Mar. 23 Recommender System I
Mar. 25 Well-Being Day — No Class
Mar. 30 Recommender System II
Apr. 01 Recommender System III
Apr. 06 Dimension Reduction I
Apr. 08 Dimension Reduction II
Apr. 13 Dimension Reduction III
Apr. 15 Final Project Presentations
Apr. 20 Final Project Presentations
Apr. 22 Final Project Presentations

Homework Tracker

All homework assignments are to be submitted via Canvas. Late homework will receive a grade of 0.

Date Assigned Instructions Due Date (Time)
Jan. 14 HW1 Jan. 31 (11:59 PM)
Jan. 28 HW2 Feb. 14 (11:59 PM)
Feb. 18 HW3 Mar. 07 (11:59 PM)
Mar. 04 HW4 Mar. 21 (11:59 PM)
Mar. 23 HW5 Apr. 04 (11:59 PM)

Final Project Details

This course includes a final project in lieu of a final exam. The project is designed to give students hands-on experience completing an end-to-end data science analysis using a real-world dataset.

Projects will be completed in groups of four and consist of:

Group List

Please form your final project group by January 28 and sign up using the shared spreadsheet. Please do not modify the information entered by other.

Individual Presentation Responsibilities

Each group will have four students:

  • One student will lead the Project Proposal Discussion.
  • A different student will lead the Midpoint Review.
  • The remaining two students will deliver the Final Project Presentation.

Each student should present at only one milestone unless approved otherwise. These roles divide only the speaking responsibilities; all members must contribute meaningfully to every stage of the project.

Five Parts Including Point Values

I will meet with each group to discuss the final project topic. Project topics can be:

  • Solve an interesting or new problem with existing method
  • Develop a new algorithm/model
  • Compare state-of-the-art algorithms on some problems
  • …

P1: Project Proposal (10 Points): The project proposal is limited to 2-page (excluding reference) and contains:

  • Problem to solve
  • Review of existing studies in this field
  • Your proposed method or Methods you would like to compare
  • Evaluation metric
  • Reference

See latex template at link.

P2: Project mid-term review (10 Points): Around the middle of the semester, each group will schedule a brief meeting with the instructor. One member from each group (different from the proposal presenter) will present a set of slides that summarize the group’s progress up to that point.

P3: Project Presentation (30 Points): All groups will present their final projects during the last week or two weeks of classes. All group members must be present in class on the day of their presentation. However, only the members who did not present in either the project proposal meeting or the mid-term review meeting are required to deliver the final presentation. The length of the presentation depends on the number of groups (10–20min) and will be announced later.

P4: Project Paper (40 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).

P5: 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.

Due Dates of Individual Parts

Part Description Location Due Date (Time)
P1 Project Proposal Canvas Jan. 27 (11:59 PM)
Proposal Discussion Hanes 334 Jan. 28 (Lecture Time)
P2 Midpoint Review Slides Canvas Feb. 28 (11:59 PM)
Midpoint Review Hanes 334 Mar. 02 / Mar. 04 (Lecture Time)
P3 Presentation Slides Canvas Apr. 14 / Apr. 19 / Apr. 21 (11:59 PM)
Final Presentation Class Apr. 15 / Apr. 20 / Apr. 22 (Lecture Time)
P4 Final Project Report and Code Canvas Apr. 30 (11:59 PM)
P5 Peer Scoring Google Survey Apr. 30 (11:59 PM)

Reading

This page was last updated on 2026-10-21 14:49:07.401624 Eastern Time.