Design and development of algorithms and techniques that allow computers to "learn" by extracting information from data automatically and by computational and statistical methods. This course covers four major concepts: supervised learning, learning theory, unsupervised learning, and reinforcement learning and control.
Machine Learning
CS229
Delivery Options: Online, At Stanford
Course Description
Topics Include
- Statistical pattern recognition
- Linear and non-linear regression
- Non-parametric methods
- Exponential family
- GLIMs
- Support vector machines
- Kernel methods
- Model/feature selection
- Learning theory
- VC dimension
- Clustering
- Density estimation
- EM
- Dimensionality reduction
- ICA
- PCA
- Reinforcement learning and adaptive control
- Markov decision processes
- Approximate dynamic programming
- Policy search
Assignments
There will be 4 assigments and a term project.
Degrees and Certificates
Prerequisite(s)
Linear algebra, and basic probability and statistics.
Recommended
We highly recommend watching the course preview to ensure you have the requisite background and understand the scope of material covered.
Primary Faculty
Andrew Ng
Assistant Professor of Computer Science
Stanford University School of Engineering
COURSE SECTION
CS229 - 013 Online, At Stanford Enrollment Closed Autumn 2009-10
| Day | Date | Time | Location |
|---|---|---|---|
| Mon, Wed | Sep 14 to Dec 09, 2009 | 9:30AM to 10:45AM | Online |
Computer Science Department Requirement
Students taking graduate courses in Computer Science must enroll for the maximum number of units and maintain a B or better in each course in order to continue taking courses under the Non Degree Option.
Non Degree Option
Note: Enrolling in this course for credit under the Non Degree Option requires an approved application. If you do not already have an approved application on record, the application will be presented to you as part of the checkout process. If your application is denied, tuition and fees for the course will be refunded.
Textbooks/Course Materials
Students enrolled in a graduate course for credit are required to complete homework assignments, projects, and take exams as required of all students during the 10-week quarter. Information regarding textbooks and materials is usually covered in the first lecture and may also be found on the course Web site.