Schedule

Fall 2025

Class Time and Location

When: Thursdays, 6:30 PM - 9:15 PM
Where: Blank Sheppard Innovation Center, Room 203

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 may not be accepted unless prior arrangements are made.
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.

Week	Date	Description	Coursework	Readings
		Module 1: Introduction and Background
1	Aug 21 [slides]	Course Overview Introduction to Machine Learning Machine Learning History An Orientation to the Major Branches of Machine Learning	Discussion (self-introduction)	Examples Link to Reading Link to Reading HML Ch. 1.4
		Module 2: Classification and Regression
2	Aug 28 [slides]	Introduction to Multiclass Classification Decision Trees for Classification (DTs) k-Nearest Neighbors (kNN) Hands-on Practice	Recap Practice Assign HW#1	Examples Link to Reading Link to Reading Link to Reading
3	Sep 4 [slides]	Naïve Bayes Classifier Support Vector Machine (SVM) Evaluation of Classifiers: Cross-Validation Hands-on Practice	HW#1 DUE Recap Practice Discussion#1	Examples Link to Reading PML Ch. 3
4	Sep 11 [slides]	One-vs-All Multiclass Classification Classifier Confidence and Estimation Ensemble Learning Hands-on Practice	Recap Practice Assign Quiz#1	Examples Link to Reading Link to Reading HML Ch. 7
5	Sep 18 [slides]	Introduction to Regression Models Linear Regression and Logistic Regression Comparing Regression and Classification Hands-on Practice	Quiz#1 DUE Recap Practice Discussion#2	Examples Link to Reading Link to Reading HML Ch. 4
		Module 3: Dimensionality Reduction and Clustering
6	Sep 25 [slides]	Natural Language Processing and Computer Vision Feature Representation and Scaling Introduction to Principal Component Analysis (PCA) Hands-on Practice	Recap Practice Assign HW#2	Examples Link to Reading Link to Reading Link to Video
7	Oct 2 [slides]	Recall the Unsupervised Learning Introduction to Clustering K-Means Clustering and The Elbow Method Hands-on Practice	HW#2 DUE Recap Practice Discussion#3	Examples Link to Reading Link to Reading Link to Reading
		Module 4: Neural Networks
8	Oct 9 [slides]	Machine Learning as Function Approximation Biological Neurons vs. Artificial Neurons Introduction to Perceptron and Adaline Hands-on Practice	Recap Practice	Examples Link to Reading Link to Reading Link to Video
9	Oct 14	Mid-Semester Break	None	Fall Break E-card
9	Oct 16 [slides]	Introduction to Neural Network Forward Propagation Backpropagation Hands-on Practice	Recap Practice Assign HW#3	Examples Link to Reading Link to Video Link to Video
		Module 5: Introduction to Deep Learning
10	Oct 23 [slides]	Challenges in Machine Learning Tools Used to Implement Deep Learning Models Introduction to Open-source Frameworks Student Presentation	HW#3 DUE Recap Installation Assign Quiz#2	Link to Reading Link to Reading Link to Reading Link to OpenML
11	Oct 30 [slides]	Autoencoders Convolutional Neural Networks (CNNs) Learning Deep Features Student Presentation	Quiz#2 DUE Practice Discussion#4	Examples Link to Reading Link to Reading Link to Reading
12	Nov 6 [slides]	Recurrent Neural Networks (RNNs) Active Learning (AL) and Curriculum Learning (CL) Reinforcement Learning (RL) Student Presentation	Recap Practice Assign HW#4	Examples Link to Reading Link to Reading Link to Video
		Module 6: Responsible AI and AI Ethics
13	Nov 13 [slides]	Social Impacts of AI Transparency, Accountability, and Fairness (TAF) in AI Ethical Considerations in AI Student Presentation	HW#4 DUE	Link to Reading Link to Reading Link to Reading Canvas Q & A
14	Nov 20	Lessons Covered Final Quiz Session	All Discussions DUE Closed-Book Quiz	No Notes Bring Laptops
15	Nov 27	Thanksgiving Break	No Class	Thxgiving E-Card
16	Dec 4	Study Day	No Class	Study Day E-Card
17	Dec 9	Final Project Presentation