Foundations of Machine Learning

1 credits · Graduate Programs

Basic statistical/computational theories and current libraries for machine learning will be introduced. Content may evolve according to the progress of the field but includes: 1. History and current status of machine learning. 2. Linear models and regularization. 3. Support vector machine and kernel methods. 4. Neural networks (including deep learning). The module introduces both theories and implementations, with a focus on applying the techniques to biological and medical big-data. The level of difficulties may be adjusted according to the diverse backgrounds of the attendees.