Schedule

Fall 2026

Class Time and Location

When: Tuesdays, 6:30 PM - 9:15 PM
Where: Richter Math-Engineering Room 103

Course Schedule

The content below includes what we will be teaching throughout the semester and is subject to change to meet the learning goals of the class. Check this website regularly for the latest schedule and for course materials that will be posted here through links on the schedule. Please refer to the table below for topics, assignments, and readings for each session. This schedule is designed to guide you through the key concepts and practical skills required for machine learning course. Note that:

Slides for each session will be posted after class and can be accessed via the links.
Description includes but not limited to the topics that will be covered in class.
Assignments are due as indicated. Late submissions will not be accepted for Finals.
Readings include both textbook chapters and selected online resources to supplement the learning process.
Important dates such as presentations, exams, and breaks are highlighted for convenience.

The textbook and/or other recommended readings can be found in the Syllabus. Additionally, office hours and contact information for the class instructor are provided in the Instructor page. Coding Examples for each week can be found in the Supplementary Materials section.

Week	Date	Description	Coursework	Readings
Module 1: Introduction to Artificial Intelligence
1	Aug 18 [slides]	Course Overview Introduction to Machine Learning Machine Learning History Major Branches of Machine Learning	Responsibility Quiz	Examples Link to Reading Link to Reading HML Ch. 1.4
Module 2: Introduction to Machine Learning
2	Aug 25 [slides]	Introduction to Multiclass Classification Decision Trees for Classification (DTs) k-Nearest Neighbors (kNN) Hands-on Practice	Assign Practice A	Examples Link to Reading Link to Reading Link to Reading
3	Sep 1 [slides]	Naïve Bayes Classifier Support Vector Machine (SVM) Evaluation of Classifiers: Cross-Validation Quiz#1 Handout	Practice A DUE Assign Practice B HW#1 (Optional)	Examples Link to Reading PML Ch. 3
4	Sep 8 [slides]	One-vs-All Multiclass Classification Classifier Confidence and Estimation Ensemble Learning Quiz#2 Handout	Practice B DUE Assign Practice C	Examples Link to Reading Link to Reading Worksheet
5	Sep 15 [slides]	Introduction to Regression Models Linear Regression and Logistic Regression Comparing Regression and Classification Quiz#3 Handout	Practice C DUE Assign Practice D	Examples Link to Reading Link to Reading HML Ch. 4
6	Sep 22 [slides]	Real-world Data Challenges Feature Representation and Scaling Coding Quiz I Review Guide Mid-term Exam I Q&A	Practice D DUE HW#2 (Optional) Q&A	Examples Link to Reading Link to Reading Review Guide
7	Sep 29	Coding Quiz I	Hybrid Mode	None
8	Oct 6	Mid-term Exam I	Lockdown Browser	None
9	Oct 13	Mid-Semester Break	None	Fall Break E-card
Module 3: Introduction to Neural Networks
10	Oct 20 [slides]	Machine Learning as Function Approximation Biological Neurons vs. Artificial Neurons Introduction to Perceptron and Adaline Student Presentation	Assign Practice E	Examples Link to Reading Link to Reading Link to Video
11	Oct 27 [slides]	Introduction to Neural Network Forward Propagation Backpropagation Student Presentation	Practice E DUE Assign Practice F HW#3 (Optional)	Examples Link to Reading Link to Video Link to Video
12	Nov 3 [slides]	Challenges in Machine Learning Introduction to Open-source Frameworks Coding Quiz II Review Guide Student Presentation	Practice F DUE Assign Proposal Q&A	Resources Link to Reading Scikit-Learn Review Guide
13	Nov 10	Coding Quiz II	Lockdown Browser	None
14	Nov 17	Mid-term Exam II	Lockdown Browser	None
15	Nov 24	Coding Quiz I Review Session	Optional Participation	Via Zoom
15	Nov 25	Thanksgiving Break	No Class	Thxgiving E-Card
Module 4: Responsible AI and AI Ethics
16	Dec 1 [slides]	Social Impacts of AI Transparency, Accountability, and Fairness (TAF) in AI Ethical Considerations in AI Course Evaluation Survey	Proposal DUE FATE Survey (Optional)	Link to Website Link to HealLab Link to Research Link to Survey
16	Dec 2	Study Day	No Class	Study Day E-Card
17	Dec 8	Final Project Report Submission	In-Person Students Only	via Canvas
17	Dec 8	Final Project Presentation	Remote Students Only	via Zoom
17	Dec 8	All ASSIGNMENTS IN THIS CLASS ARE DUE TODAY. NO LATE SUBMISSIONS WILL BE ACCEPTED AFTER THIS DATE.