Lecture 9: Approximation Of Estimation Error And Empirical Risk Minimization

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Lecture 1: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Linear Regression and Gradient Descent

Lecture 3: Locally Weighted Logistic Regression

Lecture 4: Perceptron and Generalized Linear Models

Lecture 5: Gaussian Discriminant Analysis and Naive Bayes

Lecture 6: Support Vector Machines

Lecture 7: Kernels

Lecture 8: Data Splits, Models, and Cross-Validation

Lecture 9: Approximation of Estimation Error and Empirical Risk Minimization

Lecture 10: Decision Trees and Ensemble Methods

Lecture 11: Introduction to Neural Networks

Lecture 12: Backpropagation and Improving Neural Networks

Lecture 13: Debugging Machine Learning Models and Error Analysis

Lecture 14: Expectation Maximization Algorithms

Lecture 15: EM Algorithm and Factor Analysis

Lecture 16: Independent Component Analysis and Reinforcement Learning

Lecture 17: Markov Decision Processes, Value Iteration, and Policy Iteration

Lecture 18: Continuous State MDP Model Simulation

Lecture 19: Reward Models and Linear Dynamical Systems

Lecture 20: Debugging and Diagnostics in Reinforcement Learning

Final Exam - Machine Learning: Stanford CS229 Autumn 2018

Machine Learning: Stanford CS229 Autumn 2018

This lecture focuses on estimating errors and the principles of empirical risk minimization.

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