CS760 — Machine Learning

Instructor: Daniel Pimentel-Alarcón.

Graduate-level class covering the theoretical foundations of core machine learning algorithms — from logistic regression to SVMs and neural networks. Students should have a background in linear algebra, probability, and coding.

Syllabus

Lecture Notes

Topic 1 | Overview.
Topic 2 | Review of Linear Algebra.
Topic 3 | Review of Probability.
Topic 4 | Review of Optimization.
Topic 5 | Linear Regression.
Topic 6 | Logistic Regression.
Topic 7 | Cross-Validation.
Topic 8 | Decision Trees.
Topic 9 | Nearest Neighbors.
Topic 10 | Bayesian Learning.
Topic 11 | Support Vector Machines.
Topic 12 | Neural Networks.

Homework

Homework 1 | Review.
Homework 2 | Linear Regression.
Homework 3 | Logistic Regression — titanic_data.csv .
Homework 4 | Decision Trees.
Homework 5 | Nearest Neighbors & Naive Bayes.
Homework 6 | Frequentists vs Bayesians.
Homework 7 | Presentation.

New Topics Notes

Instructions
notes_template.tex