USMA MA289: Mathematics of AI

Intro to Statistical Learning

Learning Outcomes

Understand the basic principles of statistical learning
Distinguish between supervised and unsupervised learning
Understand the bias-variance tradeoff and model evaluation concepts
Learn foundational techniques used in predictive modeling

Helpful Videos

Introduction to Machine Learning

Textbook References

Statistical Learning with Python (ISLP), Chapter 1: Introduction to Statistical Learning

Key Topics, Meeting Notes / Lesson Objectives, and Assignments

Date	Key Topics	Meeting Notes and Lesson Objectives	Assignments
13 Jan 2026	Lesson 01: Introduction to Statistical Learning 1	Meeting notes linked here. Discussed Basics of Statistical Learning, focusing on references, tools, and vocabulary. Lesson Objecitives: (1) Understand the three types of error, and (2) Understand the objectives of the course.	ISLP 1-39; MLP Mod 1
20 Jan 2026	Lesson 02: Introduction to Statistical Learning 2	Meeting notes linked here. Introduce Google Colab and find your data set. EDA assignment here. Lesson Objecitives: (1) Understand how to use course coding platform.	Read short article on EDA here. Add shortcut under your google drive "Colab Notbooks" folder using the instructor's folder linked here.
27 Jan 2026	Lesson 03: Assignment 1 Exploratory Data Analysis 1	Colab notebook with EDA examples here.Review Assignment 1: EDA. Begin Project 1, Resampling with selected data set in class. Review results of assignment, discuss the use of Overleaf(latex). Lesson Objecitives: (1) Understand common anlaysis used in EDA, and (2) Understand how to use Overleaf.	Review and select data set
03 Feb 2026	Lesson 04: Project 1 Exploratory Data Analysis 2	Meeting notes linked here. Colab CV, Resampling, and bootstrap example notebook with examples here. Complete Assignemnt 1, work on Project 1. Recieve feedback on anlysis from peers. Lesson Objecitives: (1) Understand the End to End ML, (2) Understand norms and error types, and (3) Understand resampling, CV, Bootstrapping.	Geron pp. 41-86, ISLP 201-215. Turn in Assignment 1: EDA. via google colab.