Machine Learning: Stanford CS229 Autumn 2018
Lecture 9: Approximation of Estimation Error and Empirical Risk Minimization
Machine Learning: Stanford CS229 Autumn 2018
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Lecture 1: Introduction to Machine Learning
Video - 1:15:20 mins
Lecture 1: Introduction to Machine Learning
Video - 1:15:20 mins
Lecture 2: Linear Regression and Gradient Descent
Video - 1:15:20 mins
Lecture 3: Locally Weighted Logistic Regression
Video - 1:15:20 mins
Lecture 4: Perceptron and Generalized Linear Models
Video - 1:15:20 mins
Lecture 5: Gaussian Discriminant Analysis and Naive Bayes
Video - 1:15:20 mins
Lecture 6: Support Vector Machines
Video - 1:15:20 mins
Lecture 7: Kernels
Video - 1:15:20 mins
Lecture 8: Data Splits, Models, and Cross-Validation
Video - 1:15:20 mins
Lecture 9: Approximation of Estimation Error and Empirical Risk Minimization
Video - 1:15:20 mins
Lecture 10: Decision Trees and Ensemble Methods
Video - 1:15:20 mins
Lecture 11: Introduction to Neural Networks
Video - 1:15:20 mins
Lecture 12: Backpropagation and Improving Neural Networks
Video - 1:15:20 mins
Lecture 13: Debugging Machine Learning Models and Error Analysis
Video - 1:15:20 mins
Lecture 14: Expectation Maximization Algorithms
Video - 1:15:20 mins
Lecture 15: EM Algorithm and Factor Analysis
Video - 1:15:20 mins
Lecture 16: Independent Component Analysis and Reinforcement Learning
Video - 1:15:20 mins
Lecture 17: Markov Decision Processes, Value Iteration, and Policy Iteration
Video - 1:15:20 mins
Lecture 18: Continuous State MDP Model Simulation
Video - 1:15:20 mins
Lecture 19: Reward Models and Linear Dynamical Systems
Video - 1:15:20 mins
Lecture 20: Debugging and Diagnostics in Reinforcement Learning
Video - 1:15:20 mins
Final Exam - Machine Learning: Stanford CS229 Autumn 2018
Quiz
Final Exam - Machine Learning: Stanford CS229 Autumn 2018
Quiz
Final Exam - Machine Learning: Stanford CS229 Autumn 2018
Quiz
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This lecture focuses on estimating errors and the principles of empirical risk minimization.
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