COMP 5630/6630: Machine Learning

Instructor: Jiaqi Wang
Lecture: Tue,Thu 14:00-15:15
Location: Shelby Center 1120
Office Hours: TBD
TA: Chenjia Li, chl0045@auburn.edu

Course Overview

This course covers the fundamental concepts and advanced techniques in machine learning, including decision tree learning, neural networks, deep learning, statistical learning methods, 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
08/18 (T)	Course Overview	Syllabus and logistics
08/20 (TH)	Introduction to Machine Learning
08/25 (T)	Inputs and Outputs of Machine Learning
08/27 (TH)	Hands-on Lab 1: Python and NumPy
09/01 (T)	Regression: Linear Regression 1
09/03 (TH)	Guest Speaker: Dr. Song Wang
09/08 (T)	Regression: Linear Regression 2
09/10 (TH)	Hands-on Lab 2: Kaggle and Regression
09/15 (T)	Classification: Evaluation and K Nearest Neighbors
09/17 (TH)	Classification: Logistic Regression and Perceptron
09/22 (T)	Hands-on Lab 3: Classification 1
09/24 (TH)	Classification: Decision Trees
09/29 (T)	Classification: Naive Bayesian and Support Vector Machines
10/01 (TH)	Midterm Review
10/06 (T)	Midterm Exam
10/08(TH)	Fall Break
10/13(T)	Ensemble Learning
10/15(TH)	Hands-on Lab 4: Classification 2
10/20 (T)	Clustering: Kmeans Clustering
10/22 (TH)	Hands-on Lab 5: Clustering
10/27 (T)	Deep Learning: Introduction
10/29 (TH)	PyTorch
11/03 (T)	Advanced AI Topics
11/05 (TH)	Guest Speaker
11/10 (T)	Paper Presentation
11/12 (TH)	Paper Presentation
11/17 (T)	Paper Presentation
11/19 (TH)	Paper Presentation
11/24 (T)	No Class	Thanksgiving Break
11/26 (TH)	No Class	Thanksgiving Break
12/01 (T)	Final Review
12/03 (TH)	Study at home and additionaloffice hours
12/08 (T)	Final Exam
12/10 (TH)	No Class

Grading

Homework (20%):
- There are 2 homework assignments
- They will total to 20% of the final grade.
Paper Presentation and Slides (20%):
- There is 1 paper presentation. Each student is required to present one paper.
- Each slides and each presentation count towards 10% of the final grade.
- Online students need to share the link of the video recording for the presentation.
Exams (20%)
- There is 1 midterm exam and 1 final exam.
- Each exam counts towards 10% of the final grade.
Labs (20%)
- There are 5 hands-on labs.
- Each lab counts towards 4% of the final grade.
Guest Speakers' Talk Takeaway (20%)
- There are 2 guest speaker sessions.
- Each session counts towards 10% of the final grade.
Bonus
- 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.