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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.

For more online learning opportunities, please visit Stanford Online.

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.

Material well presented by Andrew Ng

Machine Learning is a mathematically intensive field and Andrew does a good job bringing intuition to the mathematics. If you're happy with just gaining intuition into the field, you are well on track just sitting back and listening to Andrew talk about the subject. However, if you want to go beyond intuition and gain some expertise in the subject, you will have to sit with the accompanying course material and do your due diligence. At least a basic understanding of Probability, statistics and calculus is required, but Andrew keeps the entry bar low by bringing lot of intuition to the mathematics, if that's not your strength. So, this is one of those rare courses which has something for everyone. Well, almost everyone.

Excellent course

Helped me break into SVM's with minimal fuss.

Machine Learning
View In iTunes

Customer Ratings