The Selective Course for Computer Science
Learning is an internal mental process that integrates new information into established
mental frameworks and updates those frameworks over time.
This course provides a broad introduction to machine learning and some applications. Topics include:
supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks,
support vector machines); unsupervised learning (clustering, dimensionality reduction);
learning theory (bias/variance tradeoffs, VC dimension); reinforcement learning.
The course will also discuss recent applications of machine learning.