Cool and open video lectures on AI/Machine Learning by Andrew Ng.http://see.stanford.edu/see/courseInfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1
You can go to the download page and download the format of the video lectures of your choice. i.e. YouTube | iTunes | Vyew | WMV Torrent | MP4 Torrenthttp://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1
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.)