Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018

4 of 20 videos summarized

Led by Andrew Ng, 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 to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)

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Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018

Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)

Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)

Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (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 4 - Perceptron & Generalized Linear Model | 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)