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COMP 5630/6630: Machine Learning
Instructor: Jiaqi Wang
Lecture: Tue,Thu 2:00-3:15pm
Location: Shelby Center 1120
TA Office Hours: TH 3:45-4:45pm, 2105/2319 Shelby Center, by appointment
TA: Chenjia Li, chl0045@auburn.edu
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Course Overview
This course covers the fundamental concepts and advanced techniques in machine learning,
including decision tree learning, neural networks, deep learning, unsupervised learning, large language models, and reinforcement learning.
We will explore various algorithms and models, and their applications in natural language processing, computer vision, and other domains.
The course also emphasizes practical implementation and real-world case studies to provide a comprehensive understanding of the field.
Schedule
The schedule is tentative and subject to change. Please see the table below for the latest updates.
| Date |
Topics |
Notes |
Due |
| 08/18 (T) |
Course Overview |
Syllabus and logistics |
| 08/20 (TH) |
Introduction to Machine Learning |
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| 08/25 (T) |
Inputs and Outputs of Machine Learning |
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| 08/27 (TH) |
Regression: Linear Regression 1 |
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| 09/01 (T) |
Regression: Linear Regression 2 |
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| 09/03 (TH) |
Guest Speaker: Dr.Song Wang |
Assistant Professor at University of Central Florida |
| 09/08 (T) |
Classification: Logistic Regression and Perceptron |
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| 09/10 (TH) |
Project Introduction: how to do research |
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| 09/15 (T) |
Classification: K Nearest Neighbors |
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| 09/17 (TH) |
Classification: Decision Trees |
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| 09/22 (T) |
Guest Speaker: Dr.Zining Zhu |
Assistant Professor at Stevens Institute of Technology |
| 09/24 (TH) |
Clustering: Kmeans Clustering |
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| 09/29 (T) |
Guest Speaker: Dr. Jeya Maria Jose |
Senior Researcher at Microsoft Research |
| 10/01 (TH) |
Project Proposal Presentation |
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| 10/06 (T) |
Project Proposal Presentation |
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| 10/08(TH) |
Fall Break |
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| 10/13(T) |
Neural Network and Deep Learning |
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| 10/15(TH) |
Guest Speaker: Dr. Ruixiang Tang |
Assistant Professor at Rutgers University |
| 10/20 (T) |
Supervised and Unsupervised Learning |
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| 10/22 (TH) |
Guest Speaker: Dr. Hao Zhou |
Assistant Professor at University of Texas at Dallas |
| 10/27 (T) |
Advanced AI Topics: 1 |
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| 10/29 (TH) |
Guest Speaker: Dr. Yusen Zhang |
Postdoctoral Research Scientist at Columbia University |
| 11/03 (T) |
Advanced AI Topics: 2 |
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| 11/05 (TH) |
Guest Speaker: Dr. Zhu Cheng |
Applied Scientist at Amazon |
| 11/10 (T) |
Project Final Presentation |
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| 11/12 (TH) |
Project Final Presentation |
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| 11/17 (T) |
Project Final Presentation |
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| 11/19 (TH) |
Project Final Presentation |
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| 11/24 (T) |
No Class |
Thanksgiving Break |
| 11/26 (TH) |
No Class |
Thanksgiving Break |
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| 12/01 (T) |
Final Review |
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| 12/03 (TH) |
Study at home and additional office hours |
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| 12/08 (T) |
Final Exam |
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| 12/10 (TH) |
No Class |
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Grading
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Guest Speakers' Talk Takeaway (70%)
- There are 7 guest speaker sessions.
- You are asked to submit a 2 page short summary and your thoughts for each session.
- The template and instruction will be provided.
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Project (30%):
- There is 1 project presentation. Individuals or groups (max 3 people) are required to conduct a research project and present it.
- For undergraduate students: you are allowed to choose to conduct a survey/review instead of a project.
- The proposal and final presentation counts towards 5% and 5% of the final grade.
- The writing report count towards 20% of the final grade.
- Online students need to share the link of the video recording for the presentation.
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Exams (10%)
- There is 1 final exam.
- The exam counts towards 10% of the final grade.
- The exams will be open-book and open-notes, but no electronic devices are allowed.
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Bonus
- Involvement in the guest talk: Each involvement in guest speakers' talk will add 2 points to your final grades. Please email TA and me your name and the question you asked after each guest talk. Online students may add and highlight the questions in the takeaway reports.
- Idea bonus: Each submission is 1-page of what you may think is interesting or any bold reseach ideas based on the lectures.
Each submission adds 5 points to the actural grade. We totally have 2 submission opportunities.
- Paper submission: You are encouraged to submit your project paper to
AI/ML/DM conferences.
The instructor will provide a comprehensive evaluation of the work before the final grading day.
The submission is required to be reviewed and approved by the instructor. The approved paper adds 10 points to the actural grade.
Please notice that the bar of the paper submission could be (much) higher than a course project.
- Course survey: The survey completion rate >80% leads to an additional 10% for everyone’s actual grades, i.e., your_final_grade = your_actual_grade * 110%
Late Policy
- Late assignments are generally not accepted. If you are unable to meet a deadline due to extenuating circumstances, you must notify the instructor as early as possible in advance.
- May be submitted late with a 25% penalty for every 12 hours late, up to 48 hours (2 days).
After 48 hours, no credit will be given for the submission.
Academic Integrity
Auburn University is dedicated to honesty and strong moral behavior in academics. Cheating and plagiarism are expressly prohibited by the Auburn University Academic Honesty Code.
Students who attend Auburn are expected to attain high competency and deep understanding in their areas of study. While developing skills and knowledge, it is essential that Auburn students commit themselves to core principles and behaviors consistent with academic and personal integrity:
- Honesty: Upholding trust and honesty by doing your own academic work and not cheating.
- Fairness: Following correct academic procedures and practices as stated in course guidelines and as defined by Auburn University.
- Respect: Growing as a student by facing academic challenges and interacting productively with instructors.
- Responsibility: Being accountable for and accepting responsibility for class assignments and personal academic development.
More details can be found in the
Auburn University Academic Honesty Code.
Instructor's extra notes: we have
zero tolerance for academic dishonesty. If you are cheating in any forms (exams, assignment, slides, presentations, writing etc.),
you will receive a zero for the task and may fail the course. I hope we can trust and respect each other.
Accommodation Policy
Our lectures strictly follow the
Auburn University policies on accommodations for students.
If you have a disability and require accommodations, please contact the Office of Accessibility at Auburn University. Also, please discuss your accommodations and needs with the instructor as early as possible before the course begins. I will work with you to ensure that accommodations are provided appropriately.
Syllabus and Grading Change Policy
The Instructor reserves the right to make changes to the syllabus and grading policy as necessary. Any changes will be communicated to students in a timely manner.