This repository contains the final code for the first two projects of the course Advanced Machine Learning by Prof. Joachim M. Buhmann and Dr. Carlos Cotrini. The projects were graded in a competitive manner similar to a kaggle competition. Our team of three students placed 1st in project 1 and 3rd in project 2.
The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.
Topics covered in the lecture include:
Fundamentals: What is data?, Bayesian Learning, Computational learning theory
Supervised learning: Ensembles: Bagging and Boosting, Max Margin methods, Neural networks
Unsupservised learning: Dimensionality reduction techniques, Clustering, Mixture Models, Non-parametric density estimation, Learning Dynamical Systems