Machine Learning – by Stanford University (Tests+Exercises)
English | 1000×562 | h264 ~990 kbps | MP4A | 48 kHz ~112 Kbps | 1.78 GB
Mathematics, Algorihms
Genre: Video Training
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). (iv) Reinforcement learning. The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
About The Instructor
Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus. His research is primarily on machine learning, artificial intelligence, and robotics, and most universities doing robotics research now do so using a software platform (ROS) from his group.
In 2008, together with SCPD he started SEE (Stanford Engineering Everywhere), which was Stanford’s first attempt at free, online distributed education. Since then, over 200,000 people have viewed his machine learning lectures on YouTube, and over 1,000,000 people have viewed his and other SEE classes’ videos.
Ng is the author or co-author of over 100 published papers in machine learning, and his work in learning, robotics and computer vision has been featured in a series of press releases and reviews. In 2008, Ng was featured in Technology Review’s TR35, a list of “35 remarkable innovators under the age of 35″. In 2009, Ng also received the IJCAI Computers and Thought award, one of the highest honors in AI.
1. Introduction
2. Linear regression with one variable
3. (Optional) Linear algebra review
4. Linear regression with multiple variables
5. Octave tutorial
6. Logistic Regression
7. One-vs-all Classification
8. Regularization
9. Neural Networks
10. Backpropagation Algorithm
11. Practical advise for applying learning algorithms
12. How to develop and debug learning algorithms
13. Feature and model design, setting up experiments
14. Support Vector Machines (SVMs)
15. Survey of other algorithms: Naive Bayes, Decision Trees, Boosting
16. Unsupervised learning: Agglomerative clustering, k-Means, PCA
17. Combining unsupervised and supervised learning.
18. (Optional) Independent component analysis
19. Anomaly detection
20. Other applications: Recommender systems. Learning to rank
21. Large-scale/parallel machine learning and big data.
22. Machine learning design / practical methods
23. Team design of machine learning systems
Professor: Andrew Ng
Run time: ~19 hours
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