CS 229 Machine Learning +CS221Artificial Intelligence: Principles and Techniques

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CS 229 Machine Learning +CS221Artificial Intelligence: Principles and Techniques
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Course Materials
There is no required text for this course. Notes will be posted periodically on the course web site. The following books are recommended as optional reference:
1.Christopher Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
2.Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley &Sons, 2001.
3.Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
4.Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press,1998


AI Overview
• CS221 (Aut): Artificial Intelligence: Principles and Techniques. Broad overview
of AI and applications, including robotics, vision, NLP, search, Bayesian networks,
and learning. Taught by Professor Andrew Ng.
Robotics
• CS223A (Win): Robotics from the perspective of building the robot and controlling
it; focus on manipulation. Taught by Professor Oussama Khatib (who builds the
big robots in the Robotics Lab).
• CS225A (Spr): A lab course from the same perspective, taught by Professor Khatib.
• CS225B (Aut): A lab course where you get to play around with making mobile
robots navigate in the real world. Taught by Dr. Kurt Konolige (SRI).
• CS277 (Spr): Experimental Haptics. Teaches haptics programming and touch
feedback in virtual reality. Taught by Professor Ken Salisbury, who works on
robot design, haptic devices/teleoperation, robotic surgery, and more.
• CS326A (Latombe): Motion planning. An algorithmic robot motion planning
course, by Professor Jean-Claude Latombe, who (literally) wrote the book on the
topic


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.







CS 229 Machine Learning.zip (6.73 MB, 需要: RMB 39 元)

2023-10-8 03:19:51 上传

CS 229 Machine Learning
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关键词：Intelligence Techniques Artificial Principles principle

感谢楼主，做的很详细，很靠谱，多谢楼主分享啊