Stanford EE263: Introduction to Linear Dynamical Systems
Introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems.
http://www.stanford.edu/~boyd/ee263/index.html
http://www.youtube.com/playlist?list=PL06960BA52D0DB32B
MIT - 18.085 Computational Science and Engineering I
Review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: differential equations of equilibrium; Laplace's equation and potential flow; boundary-value problems; minimum principles and calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications.
http://ocw.mit.edu/courses/mathematics/18-085-computational-science-and-engineering-i-fall-2008/index.htm
Statistical Aspects of Data Mining (Stats 202 - Google/Stanford)
Topics: decision trees, neural networks, association rules, clustering, case based methods, and data visualization.
http://www.youtube.com/playlist?list=PLA40054B49BA80084&feature=view_all
http://www.stats202.com/original_index.html
Added 2011:
Awesome, free, online courses from Stanford offered Oct 2011:
CS229, Machine Learning Course
Broad introduction to machine learning, datamining, and statistical pattern recognition
http://www.ml-class.org/
http://cs229.stanford.edu/
http://www.reddit.com/r/mlclass/
CS221, Artificial Intelligence Course
Covering basic elements of AI, such as knowledge representation, inference, machine learning, planning and game playing, information retrieval, and computer vision and robotics.
http://www.ai-class.com/
http://robots.stanford.edu/cs221/
http://www.reddit.com/r/aiclass
Unsupervised Feature Learning and Deep Learning
Video lectures by Andrew Ng (Stanford, also teaches CS229)
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=ufldl
Added 2010:CS229, Machine Learning Course
Broad introduction to machine learning, datamining, and statistical pattern recognition
http://www.ml-class.org/
http://cs229.stanford.edu/
http://www.reddit.com/r/mlclass/
CS221, Artificial Intelligence Course
Covering basic elements of AI, such as knowledge representation, inference, machine learning, planning and game playing, information retrieval, and computer vision and robotics.
http://www.ai-class.com/
http://robots.stanford.edu/cs221/
http://www.reddit.com/r/aiclass
Unsupervised Feature Learning and Deep Learning
Video lectures by Andrew Ng (Stanford, also teaches CS229)
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=ufldl
Other interesting lecture series:
Berkeley Computer Science 61A, 001 - Spring 2008:- http://www.youtube.com/view_play_list?p=6879A8466C44A5D5
- http://wla.berkeley.edu/~cs61a/fa10/
- Covers different types of programming, including functional, OO, mapreduce etc.
Stanford Data Mining CS246/CS341 (formerly CS345A)
- Winter 2011 Homepage
- Winter 2010 Handouts
- Winter 2009 Handouts
- Sadly no youtube lectures :( But Anand Rajaraman and Jeffrey D. Ullman have released a book based on the course: Mining of Massive Datasets.
