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Artificial Intelligence. Stanford School of Engineering
WMV3 | English | 640x480 | WMV | 29.970 fps 416 kbps | MP3 20 kbps | 11.4 GB
Genre: eLearning

Description:
- INTRODUCTION TO ROBOTICS (CS223A)
COURSE CONTENT:
Instructor: Khatib, Oussama
The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.
The course is presented in a standard format of lectures, readings and problem sets. There will be an in-class midterm and final examination. These examinations will be open book. Lectures will be based mainly, but not exclusively, on material in the Lecture Notes book. Lectures will follow roughly the same sequence as the material presented in the book, so it can be read in anticipation of the lectures

- MACHINE LEARNING (CS229)
COURSE CONTENT:
Instructor: Ng, Andrew
This course provides a broad introduction to machine learning and statistical pattern recognition.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.)
- NATURAL LANGUAGE PROCESSING (CS224N)
COURSE CONTENT:
Instructor: Manning, Christopher D.
This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Wordlevel, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.
Prerequisites:
- Adequate experience with programming and formal structures (e.g., CS106B/X and CS103B/X).
- Programming projects will be written in Java 1.5, so knowledge of Java (or a willingness to learn on your own) is required.
- Knowledge of standard concepts in artificial intelligence and/or computational linguistics (e.g., CS121/221 or Ling 180).
- Basic familiarity with logic, vector spaces, and probability. Intended Audience:
- Graduate students and advanced undergraduates specializing in computer science, linguistics, or symbolic systems.
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