Elective:AlgorithmsOfMachineLearning.tex

Algorithms of Machine Learning

This course teaches students machine learning - the field of study about algorithms that automatically adapt to the analyzed data. This field of study is widely used and actively developing due to growing volumes of collected data and increasing computational resources. During the course students will learn how to make predictive models (classification, regression), how to reduce dimensionality of the data (using feature selection and feature extraction), how to group data into logical categories (clustering), and how to make recommendation systems (collaborative filtering). All these tasks are very common in data rich industries such as web search, telecommunications, banking, marketing and many others. The most famous and widely used algorithms suited to solve these problems are presented. For each algorithm its mathematical model, data assumptions, advantages and disadvantages as well as connections with other algorithms are analyzed to provide an in-depth and critical understanding of the subject. Much attention is given to developing practical skills during the course. Students are asked to apply studied algorithms to real datasets for solving practical problems. Machine learning algorithms are applied using python programming language and its scientific extensions (scikit-learn, numpy, scipy, pandas, matplotlib, seaborn), which are briefly taught during the course.