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CS229 - Machine Learning

CS229: Machine Learning. Instructor: Prof. Andrew Ng, Department of Computer Science, Stanford University. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Lecture 09 - Approx/Estimation Error and ERM


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Lecture 01 - Introduction and Basic Concepts
Lecture 02 - Linear Regression and Gradient Descent
Lecture 03 - Locally Weighted and Logistic Regression
Lecture 04 - Perceptron and Generalized Linear Model
Lecture 05 - GDA and Naive Bayes
Lecture 06 - Support Vector Machines
Lecture 07 - Kernels
Lecture 08 - Data Splits, Models and Cross-Validation
Lecture 09 - Approx/Estimation Error and ERM
Lecture 10 - Decision Trees and Ensemble Methods
Lecture 11 - Introduction to Neural Networks
Lecture 12 - Backprop and Improving Neural Networks
Lecture 13 - Debugging ML Models and Error Analysis
Lecture 14 - Expectation-Maximization Algorithms
Lecture 15 - EM Algorithm and Factor Analysis
Lecture 16 - Independent Component Analysis and RL
Lecture 17 - MDPs and Value/Policy Iteration
Lecture 18 - Continuous State MDP and Model Simulation
Lecture 19 - Reward Model and Linear Dynamical System
Lecture 20 - RL Debugging and Diagnostics