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.

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	Examples Link to Reading PML Ch. 3
4	Sep 11 [slides]	Introduction to Regression Models Linear Regression Comparing Regression and Classification Hands-on Practice	Recap Practice Assign Quiz#1
		Module 3: Dimensionality Reduction and Clustering
5	Sep 18 [slides]	Natural Language Processing and Computer Vision Feature Representation and Scaling Introduction to Principal Component Analysis (PCA) Hands-on Practice	Quiz#1 DUE Recap Practice Discussion
6	Sep 25 [slides]	Recall the Unsupervised Learning Introduction to Clustering K-Means Clustering and The Elbow Method Hands-on Practice	Recap Practice Assign HW#2	Example
		Module 4: Neural Networks
7	Oct 2 [slides]	Machine Learning as Function Approximation Multi-layer Perceptron (MLP) Cost/Loss Functions Hands-on Practice	HW#2 DUE Recap Practice Discussion
8	Oct 9 [slides]	Introduction to Gradient Descent Stochastic Gradient Descent Optimization and Regularization Hands-on Practice	Recap Practice Assign HW#3
9	Oct 16 [slides]	Network Architecture Forward Propagation Backpropagation Hands-on Practice	HW#3 DUE Recap Practice Discussion
		Module 5: Introduction to Deep Learning
10	Oct 23 [slides]	Overview of Deep Learning Models Libraries Used to Implement Deep Learning Models Introduction to TensorFlow and PyTorch Hands-on Practice	Recap Practice Assign Quiz#2
11	Oct 30 [slides]	Autoencoders Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Hands-on Practice	Quiz#2 DUE Recap Practice Discussion
12	Nov 6 [slides]	Introduction to Reinforcement Learning Markov Decision Processes (MDPs) Policy, Value, and Rewards Functions Hands-on Practice	Recap Practice Assign HW#4
13	Nov 13 [slides]	Modern Generative AI GPT and Large Language Models (LLMs) Prompt Engineering Fun Practice	HW#4 DUE Recap Practice Discussion
		Module 6: Responsible AI
14	Nov 20 [slides]	Social Impacts of AI Ethical Considerations in AI Fairness, Accountability, and Transparency Final Quiz Session	All Discussions DUE Final Quiz
15	Nov 27	Thanksgiving Break	No Class
16	Dec 4	Study Day	No Class
17	Dec 9	Final Project Presentation