index.md

(CS 229) Introduction to Machine Learning [Autumn 2018]

References

• Video Lectures
• Course Website

Info

• Stanford, Andrew Ng
• Lecture 1 to Lecture 10
• Core of Supervised Machine Learning (No Neural Networks)

Content

1. Introduction
2. Linear Regression and Gradient Descent
3. Locally Weighted Linear Regression and Logistics Regression
4. Generalized Linear Model
5. GDA and Naive Bayes
6. Optimal Margin Classifier
7. SVM and Kernels
8. Bias and Variance, Regularization, Data Splits, Models and Cross Validation
9. Learning Theory
10. Decision Trees and Ensemble Methods