Data Science and Machine Learning 2: Tools
Type: core for class of 2017 Sept / elective for class of Mar 2017
Timing: Friday afternoon + a weekday 5.30
This course will build on the previous one (which introduced the basic concepts in machine learning) and will discuss state-of-the-art algorithms for supervised learning (linear models, lasso, decision trees, random forests, gradient boosting machines, neural networks, support vector machine, deep learning etc.). A large part of the course will be dedicated to using (hands-on) the software tools for machine learning used by data scientists in practice (various high-performance R packages, h2o, xgboost, libraries for deep learning on GPUs etc.).