Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng provides an overview of the course in this introductory meeting.

1. Machine Learning Lecture 1

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.

2. Machine Learning Lecture 2

science, math, engineering, computer, technology, robotics, algebra, locally, weighted, logistic, regression, linear, probabilistic, interpretation, Gaussian, distribution, digression, perceptron

3. Machine Learning Lecture 3

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning.

4. Machine Learning Lecture 4

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning.

5. Machine Learning Lecture 5

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine.

6. Machine Learning Lecture 6

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals.

7. Machine Learning Lecture 7

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels.

8. Machine Learning Lecture 8

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities.

9. Machine Learning Lecture 9

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection.

10. Machine Learning Lecture 10

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms.

11. Machine Learning Lecture 11

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization.

12. Machine Learning Lecture 12

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning.

13. Machine Learning Lecture 13

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng continues his discussion on factor analysis and expectation-maximization steps.

14. Machine Learning Lecture 14

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning.

15. Machine Learning Lecture 15

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.

16. Machine Learning Lecture 16

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs and discretization.

17. Machine Learning Lecture 17

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses state action rewards and linear dynamical systems in the context of linear quadratic regulation.

18. Machine Learning Lecture 18

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng lectures on the debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian.

19. Machine Learning Lecture 19

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.  Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning.

20. Machine Learning Lecture 20

I took this course and it is the way to get started with fundementals of ML.

awesome course

Really super course. Thank you prof. Ng and Stanfor U.

Thank you so much

Machine Learning

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