Schedule

Fall 2025

Class Time and Location

When: Mondays, 6:30 PM - 9:15 PM
Where: Virtual via Zoom (link will be provided on Canvas)

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
		Module 1: Introduction and Background
1	Jun 8 [slides]	Course Overview Introduction to Data Science Introduction to Large Language Models Setting up Coding Environment
		Module 2: Introduction to Data Science
2	Jun 15 [slides]	The Definition of Data Sampling Bias and Probability Samples Multinomial and Binomial probabilities Hands-on Practice
3	Jun 22 [slides]	Introduction to Pandas Data Manipulation with Pandas Handy Utility Functions in Pandas Hands-on Practice
4	Jun 29 [slides]	Introduction to Data Wrangling Principles of Exploratory Data Analysis (EDA) Regular Expressions for Data Cleaning Hands-on Practice
5	Jul 6 [slides]	Introduction to Scientific Visualization Visualization with Matplotlib and Seaborn Kernel Density Functions for Data Visualization Hands-on Practice
6	Jul 13	Coding Quiz Session	None
		Module 3: Introduction to Large Language Models
7	Jul 20 [slides]	Introduction to Large Language Models (LLMs) Applications of LLMs in Data Science Prompt Engineering Techniques Hands-on Practice
8	Jul 27 [slides]	Social Impacts of AI Transparency, Accountability, and Fairness (TAF) in AI Ethical Considerations in AI Course Review and Final Project Q&A	All Discussions DUE
9	Aug 3	Final Project Presentation