Stanford Engineering Everywhere (SEE) expands the Stanford experience to students and educators online and at no charge. A computer and an Internet connection are all you need. The SEE course portfolio includes one of Stanford's most popular sequences: the three-course Introduction to Computer Science, taken by the majority of Stanford’s undergraduates, as well as more advanced courses in artificial intelligence and electrical engineering.
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.
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.
