Delivered at:: Big Data and Information Retrieval School

Course type:: Compulsory course

When:: 3 year, 3, 4 module

Instructors

Бритков Радомир Александрович

Hushchyn, Mikhail

Matyushin, Leonid

Хатбуллина Лейля Равилевна

Shestakoff, Andrey

Full Syllabus

Abstract

This course presents the foundations of rapidly developing scientific field called intellectual data analysis or machine learning. This field is about algorithms that automatically adjust to data and extract valuable structure and dependencies from it. The automatic adjustment to data by machine learning algorithms makes it especially convenient tool for analysis of big volumes of data, having complicated and diverse structure which is a common case in modern "information era". During this course most common problems of machine learning are considered, including classification, regression, dimensionality reduction, clustering, collaborative filtering and ranking. The most famous and widely used algorithms suited to solve these problems are presented. For each algorithm its 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 data, critically analyze their output and solve theoretical problems highlighting important concepts of the course. Machine learning algorithms are applied using python programming language and its scientific extensions, which are also taught during the course. The course is designed for students of the bachelor program "Software Engineering" at the Faculty of Computer Science, HSE.

Learning Objectives

make students familiar with the major problems of data analysis, solved with machine learning (classification, regression, dimensionality reduction, clustering, collaborative filtering and ranking)
make students acquainted with the major algorithms to solve stated problems
give students a critical understanding of the subject, highlighting the limitations of each algorithm, data assumptions each algorithm relies upon, its strengths and weaknesses
teach students one of the most commonly used tools for machine learning: python programming language together with its major data analysis libraries - numpy, scipy, pandas, matplotlib and machine learning library scikit-learn
give students practical experience from application of studied methods to real datasets

Expected Learning Outcomes

to know major problems of data analysis, solved with machine learning
to understand scientific articles about data analysis and machine learning
to know major algorithms to solve stated problems
to understand dependencies between algorithms, their advantages and disadvantages
to know python programming language together with its major data analysis libraries - numpy, scipy, pandas, matplotlib and machine learning library scikit-learn
to understand, which kinds of algorithms are more appropriate for what kinds of data
to know the whole pipeline of research & development of machine learning methods
to know, how to transform data to make it more suitable for machine learning algorithms

Course Contents

Introduction to data science and machine learning
K nearest neighbours method
Decision trees
Model evaluation
Linear classifier methods
Support vector machines
Regression
Boosting
Other ensemble methods: bagging, RandomForest, etc.
Feed Forward Neural networks
Convolutional Neural networks
Feature selection and dimentionality reduction
Introduction to NLP
Clustering
Introduction to recommendation systems

Assessment Elements

Домашнее задание
Коллоквиум
Соревнования
Participation in machine learning competition is aimed to give students an opportunity to get extra points and to get practical experience of application of studied methods to real data analysis task. The task is to make a prediction system and [competition score] is set according to the accuracy of the developed prediction system.
Экзамен
Экзамен устный в Zoom. Без прокторинга. На экзамене можно пользоваться всеми своими материалами. Возможно выставление оценки автоматом. Технические требования: web-камера, микрофон, наушники / колонки, Zoom.

Interim Assessment

Interim assessment (4 module)
[score]=0.7*[cumulative score] + 0.3*[exam score] where [cumulative score] = 0.8*[homework score] + 0.2*[colloquium score] + 0.2*[competition score]

Bibliography

Recommended Core Bibliography

Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.
Mohri, M., Talwalkar, A., & Rostamizadeh, A. (2012). Foundations of Machine Learning. Cambridge, MA: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=478737

Recommended Additional Bibliography

Murphy, K. P. (2012). Machine Learning : A Probabilistic Perspective. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=480968

Bachelor’s Programme 'Software Engineering'

Contacts

Accredited by the Engineering Commission of ABET

Data Analysis

Author