This course provides an introduction to the field of machine learning, covering basic models such as linear regression and logistic regression, as well as more advanced techniques such as support vector machines (SVMs) and neural networks. Topics also include decision trees, ensemble methods, expectation-maximization algorithms, independent component analysis, reinforcement learning and linear dynamical systems. It also covers topics such as data splits, model selection and cross-validation, debugging, and diagnostics. Finally, the lectures further explore the fundamentals of machine learning, such as gradient descent, moments, and optimization, in order to lay the foundations for advanced machine learning topics.
Stanford CS229 - Machine Learning Full Course Taught by Andrew Ng - Autumn 2018
Stanford University via YouTube
Overview
Syllabus
Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018).
Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018).
Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018).
Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018).
Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018).
Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018).
Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 9 - Approx/Estimation Error & ERM | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 11 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 12 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 13 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 14 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 15 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018.
Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning Andrew Ng (Autumn2018).
Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018).
Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018).
RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018).
Taught by
Stanford Online
