Machine Learning
by Stanford
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Course Description
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; VC theory; large margins); 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.
Students are expected to have the following background:
- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)
This Stanford course was taught on campus twice per week in 75 minute lectures for the Stanford Engineering Everywhere Initiative.
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; VC theory; large margins); 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.
Students are expected to have the following background:
- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)
This Stanford course was taught on campus twice per week in 75 minute lectures for the Stanford Engineering Everywhere Initiative.
| Name | Description | Time | Price | ||
|---|---|---|---|---|---|
| 1 | 1. Machine Learning Lecture 1 | -- | 1:08:39 | Free | View In iTunes |
| 2 | Linear Algebra Review and Reference | -- | -- | Free | View In iTunes |
| 3 | Probability Theory Review | -- | -- | Free | View In iTunes |
| 4 | Convex Optimization Overview Part I | -- | -- | Free | View In iTunes |
| 5 | Convex Optimization Overview Part II | -- | -- | Free | View In iTunes |
| 6 | Hidden Markov Models | -- | -- | Free | View In iTunes |
| 7 | Gaussian Processes | -- | -- | Free | View In iTunes |
| 8 | 2. Machine Learning Lecture 2 | -- | 1:16:15 | Free | View In iTunes |
| 9 | Linear Regression, Classification and Logistic Regression, Generalized Linear Models | -- | -- | Free | View In iTunes |
| 10 | 3. Machine Learning Lecture 3 | -- | 1:13:13 | Free | View In iTunes |
| 11 | Problem Set 1 | -- | -- | Free | View In iTunes |
| 12 | 4. Machine Learning Lecture 4 | -- | 1:13:06 | Free | View In iTunes |
| 13 | 5. Machine Learning Lecture 5 | -- | 1:15:30 | Free | View In iTunes |
| 14 | Generative Learning Algorithms | -- | -- | Free | View In iTunes |
| 15 | 6. Machine Learning Lecture 6 | -- | 1:13:08 | Free | View In iTunes |
| 16 | 7. Machine Learning Lecture 7 | -- | 1:15:44 | Free | View In iTunes |
| 17 | Support Vector Machines | -- | -- | Free | View In iTunes |
| 18 | Problem Set 1 Solutions | -- | -- | Free | View In iTunes |
| 19 | 8. Machine Learning Lecture 8 | -- | 1:17:18 | Free | View In iTunes |
| 20 | Learning Theory | -- | -- | Free | View In iTunes |
| 21 | Problem Set 2 | -- | -- | Free | View In iTunes |
| 22 | 9. Machine Learning Lecture 9 | -- | 1:14:18 | Free | View In iTunes |
| 23 | 10. Machine Learning Lecture 10 | -- | 1:12:55 | Free | View In iTunes |
| 24 | Regularization and Model Selection | -- | -- | Free | View In iTunes |
| 25 | Midterm Exam | -- | -- | Free | View In iTunes |
| 26 | 11. Machine Learning Lecture 11 | -- | 1:22:18 | Free | View In iTunes |
| 27 | The Perceptron and Large Margin Classifiers | -- | -- | Free | View In iTunes |
| 28 | Problem Set 2 Solutions | -- | -- | Free | View In iTunes |
| 29 | 12. Machine Learning Lecture 12 | -- | 1:14:22 | Free | View In iTunes |
| 30 | K-Means Clustering Algorithm | -- | -- | Free | View In iTunes |
| 31 | Mixtures of Gaussians and the EM Algorithm | -- | -- | Free | View In iTunes |
| 32 | Problem Set 3 | -- | -- | Free | View In iTunes |
| 33 | 13. Machine Learning Lecture 13 | -- | 1:14:56 | Free | View In iTunes |
| 34 | The EM Algorithm | -- | -- | Free | View In iTunes |
| 35 | Factor Analysis | -- | -- | Free | View In iTunes |
| 36 | 14. Machine Learning Lecture 14 | -- | 1:20:39 | Free | View In iTunes |
| 37 | Principal Components Analysis | -- | -- | Free | View In iTunes |
| 38 | 15. Machine Learning Lecture 15 | -- | 1:17:17 | Free | View In iTunes |
| 39 | Independent Components Analysis | -- | -- | Free | View In iTunes |
| 40 | Problem Set 3 Solutions | -- | -- | Free | View In iTunes |
| 41 | 16. Machine Learning Lecture 16 | -- | 1:13:05 | Free | View In iTunes |
| 42 | Reinforcement Learning and Control | -- | -- | Free | View In iTunes |
| 43 | Problem Set 4 | -- | -- | Free | View In iTunes |
| 44 | 17. Machine Learning Lecture 17 | -- | 1:16:59 | Free | View In iTunes |
| 45 | 18. Machine Learning Lecture 18 | -- | 1:16:37 | Free | View In iTunes |
| 46 | 19. Machine Learning Lecture 19 | -- | 1:15:54 | Free | View In iTunes |
| 47 | Problem Set 4 Solutions | -- | -- | Free | View In iTunes |
| 48 | 20. Machine Learning Lecture 20 | -- | 1:16:39 | Free | View In iTunes |
| Total: 48 Episodes |
Customer Reviews
Complex but awesome course
This is certainly a very math intensive courses, but Andrew Ng is a great professor! This was one of the best classes I took while at Stanford... don't know if it quite does it justice here, but a must take if you have interest in the field.
