Course Description

This course, held by Yandex School of Data Analysis, will teach students machine learning – the field of study about algorithms that automatically adapt to the analyzed data. It is widely used and an actively developing subject due to growing volumes of collected data and increasing computational capabilities. 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) and how to group data into logical categories (clustering). The course is oriented on students with none or introductory level of experience in machine learning. However firm mathematical background is expected, because each method will be stated formally with main properties derived analytically and students will be asked to solve theoretical problems highlighting important concepts of the course.

Much attention is given to developing practical skills during the course. Students will be asked to apply studied algorithms to solve practical tasks on real data. Application projects will cover machine learning use-cases from various fields, with special emphasis on applications in high energy physics. The main tool throughout the course will be the Python programming language with its scientific libraries.

Topics and prerequisites

Topics, covered by the course: data preprocessing, density estimation, dimensionality reduction, feature selection, classification, regression,  clustering, model evaluation.

Prerequisites: mathematical analysis, linear algebra, theory of probability and statistics, general programming skills.

Prior acquaintance with Python programming language and its major data analysis libraries (numpy, scipy, matplotlib, pandas) is desirable. You may obtain it using, for example,
A Crash Course in Python for Scientists, Getting started for Python with Science and 10 minutes to pandas.

Registration and fee

You may book a place via this form. The course fee is £260 per person.