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No Strings Attached
- Author:
-
Andrew Ng
- Subject:
- Mathematics and Statistics, Science and Technology
- Institution Name:
- Stanford University
- Collection:
-
Stanford University - School of Engineering
- Grade Level:
- Post-secondary
- Abstract:
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.
- Languages:
- English
- Material Type:
- Audio Lectures, Full Course, Lecture Notes, Video Lectures
- Media Format:
- Audio, Text/HTML, Downloadable docs, Video
- Conditions of Use:
-
Creative Commons Attribution 3.0
- Copyright Holder:
- Stanford University
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
Artificial Intelligence: Machine Learning
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