Stanford / Computer Science / Digital Imaging Processing
Lecture : The Motivation & Applications of Machine Learning
By Andrew Ng | Artificial Intelligence - Machine Learning
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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:

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.)
Courses Index
1 : Introduction to Robotics   (Oussama Khatib / Stanford)
2 : Special Topics in Computer Science   (Multiple Instructors / University of Washington)
3 : Cognitive Science   (Geoffrey Nunberg / Berkeley)
4 : Image Processing and Analysis   (Owen Carmichael / UC Davis)