Machine learning

About the course

The course introduces the basic grounding in concepts and a range of model based and algorithmic machine learning methods, as well as practical design and evaluation of some machine learning solutions.
 
The course is divided into three parts:
Part 1: Principles, 4 credits.
This part introduces the background and important applications of machine learning. The following topics will be covered: support vector machines, naive bayes, k-nearest neighbor, decision trees, neural networks, linear regression, clustering, hidden markov model, and reinforcement learning. Meanwhile, a series of important concepts and knowledge will be mentioned including bias/variance tradeoffs, generative/discriminative learning, kernel methods, parametric/non-parametric learning, graphic models, and deep learning. Some machine learning libraries (e.g. scikit- learn) and development tool will be briefly introduced.
 
Part 2: Seminar, 0.5 credits.
Research papers on some specific machine learning topics will be provided in advance to all the students for reading. In this part, the students are divided into groups and are required to give short presentations to explain and assess the assigned paper (i.e. the proposed solution, datasets, experimental design etc.).  One group representative can be chosen to present but all group members must be present and involved in discussion. The teacher and other students will ask questions and give feedback.
 
Part 3: Practice, 3 credits.
This part consists of 2-4 mandatory practical assignments. The students are required to apply some of the machine learning algorithms/models and frameworks/libraries from Part 1 into practice by solving the assigned problems in real applications (e.g. text classification, search engine, or recommendation system)