Lectures
Introduction
- Lecture 01: Course Overview
- Lecture 02: Machine Learning Motivation
- Lecture 03: Scientific Computing with Python
- Lecture 04: SkiKit Learn and Nearest Neighbor Classifier
How do machines learn?
- Lecture 05: Learning Theory and Gradient Descent
- Lecture 06: Gradient Descent and Linear Regression
- Lecture 07: Linear Regression
- Lecture 08: Logistic Regression
- Lecture 09: Data Splits and Overfitting
- Lecture 10: Bias, Variance, and Regularization
Decision Trees
- Lecture 11: Regularization and Decision Trees
- Lecture 12: Decision Trees
- Lecture E1: Exam 1 Review
- Lecture 13: Ensemble Methods