Introductory Machine Learning

S&DS 265 introduces some of the key ideas and techniques in machine learning. Algorithms and concepts are presented to build intuition for how different methods work, without advanced mathematics. Assignments give students hands-on experience with the methods on different types of data. Topics include linear regression and classification, tree-based methods, topic models, language models, word embeddings, two-layer and recurrent neural networks, reinforcement learning, and an introduction to deep learning. Examples come from a variety of sources including political speeches, archives of scientific articles, real estate listings, and natural images. Programming is central to the course, and is based on the Python programming language.

Computing for the course uses Python in Jupyter notebooks. All of the the notebooks can be run in Google Colab by clicking on the icon.

Calendar Spring 2026

Lectures: Monday/Wednesday 1:00-2:15pm
Davies Auditorium

Complementary readings marked ISL refer to sections in the book An Introduction to Statistical Learning (Python version, July 2023). Assignments and quizzes are posted and due on Thursday in a given week.

Background notes on probability and linear algebra from Kevin Murphy’s book Probabilistic Machine Learning: An Introduction are here (probability) and here (linear algebra).

Week	Dates	Topics	Demos & Tutorials	Lecture Slides	Readings and Notes	Assignments & Exams
1	Jan 12, 14	Course overview; Python and background concepts	Python elements Covid trends	Mon: Course overview Wed: Python and Pandas	Data8 Chapters 3, 4, 5
2	Jan 21, 23	Linear regression and classification	Covid trends (revisited) Classification examples	Mon: MLK day Wed: Regression concepts Fri: Classification	ISL Sections 3.1, 3.2, 3.5 Notes on regression ISL Sections 4.3, 4.4 Notes on classification	Assn 1 out
3	Jan 26,28	Stochastic gradient descent	SGD examples	Mon: Classification (continued) Wed: Stochastic gradient descent	ISL Section 6.2.2 ISL Section 10.7.2	Quiz 1
4	Feb 2, 4	Bias and variance, cross-validation	Bias-variance tradeoff Covid trends (revisited) California housing	Mon: Bias and variance Wed: Cross-validation	ISL Section 2.2 ISL Section 5.1	Assn 1 in Assn 2 out
5	Feb 9, 11	Tree-based methods and principal components	Trees and forests Visualizing trees	Mon: Trees Wed: Forests	ISL Sections 8.1, 8.2 ISL Section 12.2	Quiz 2
6	Feb 16, 18	PCA and dimension reduction	PCA demo 1 PCA demo 2 PCA dimension reduction Word embeddings	Mon: PCA Wed: Embeddings and language models	ISL Section 12.2	Assn 2 in Assn 3 out converter notebook
7	Feb 23, 25	Language models, Bayes	Bayesian inference	Mon: Language models Wed: Bayesian inference	OpenAI: Better language models Notes on Bayesian inference	Quiz 3
8	March 2,4	Topic models Midterm exam (in class)	Topic models	Mon: Topic models Wed: Exam	On Canvas: Practice midterms / Sample solns Midterm / Sample soln
9	Mar 23,25	Introduction to neural networks	Tensorflow playground Minimal neural network Regression examples	Mon: Neural networks Wed: Neural networks (continued)	Notes on backpropagation ISL Sections 10.1, 10.2	Assn 3 in Assn 4 out
10	Mar 30, Apr 1	Reinforcement learning	Q-learning	Mon: Q-learning Wed: Reinforcement learning		Quiz 4
11	Apr 6, 8	Deep neural networks and LLMs	Autoencoder examples	Mon: Autoencoders Wed: LLMs	ISL Section 10.7	Assn 4 in Assn 5 out
12	Apr 13, 15	Transformers and LLMs	Attention GPT-4 Python API	Mon: Transformers Wed: LLM post-processing		Quiz 5
13	Apr 20, 22	Societal issues for machine learning		Mon: Panel discussion Wed: Course wrap up, iML Survivor		Assn 5 in
14	Monday May 4, 2pm	Final exam			Registrar: Final exam schedule Practice finals