Feature extraction is an essential pre-processing step to pattern recognition and machine learning problems. Classical algorithms of feature construction and feature selection will be introduced, with applications in image processing, text processing, genomics and proteomics, and drug screening.

This class is a weekly reading group discussing research papers on causality inference from observational or experimental data. The selected papers aim at understanding machine learning techniques to infer causality, including causal graphs derived from "graphical models”.

This class is a weekly reading group discussing research papers on causality inference from observational or experimental data. The selected papers aim at understanding machine learning techniques to infer causality, including causal graphs derived from "graphical models”.