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Artificial Intelligence Sanford University- Machine Learning

 


Artificial Intelligence Sanford University- Machine Learning

 


Mathematics, Algorihms | English | h264 1000x562 15fps | MP4A 48000 Hz | 1.78 GB

 


Genre: Video Training

 


The course "Machine Learning" conducted by the fall of 2011 Stanford University (USA, California). Leading the course: Professor Andrew Ng course consists of 19 parts ????? video lessons, followed by tests and assignments for (practical work). The distribution included all course materials, including tests, examinations, programming exercises (and answers to them.) Complete list of courses conducted by the university in fall 2011.

"Artificial Intelligence Sanford University- Machine Learning"

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Artificial Intelligence Sanford University- Machine Learning

 


Artificial Intelligence Sanford University- Machine Learning

 


Mathematics, Algorihms | English | h264 1000x562 15fps | MP4A 48000 Hz | 1.78 GB

 


Genre: Video Training

 


The course "Machine Learning" conducted by the fall of 2011 Stanford University (USA, California). Leading the course: Professor Andrew Ng course consists of 19 parts ????? video lessons, followed by tests and assignments for (practical work). The distribution included all course materials, including tests, examinations, programming exercises (and answers to them.) Complete list of courses conducted by the university in fall 2011.

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:

 


Prerequisites: ????? 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.)

 


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




Artificial Intelligence Sanford University- Machine Learning




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