Кто читает:: Департамент больших данных и информационного поиска

Статус:: Курс обязательный

Когда читается:: 3-й курс, 1, 2 модуль

Преподаватели

Воронкова Анастасия Михайловна

Кохтев Вадим Михайлович

Кузина Анна Вадимовна

Нарцев Андрей Дмитриевич

Пащенко Анатолий Владиславович

Полунина Полина Алексеевна

Тихонова Мария Ивановна

Ульянкин Филипп Валерьевич

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

distinguish major problems of data analysis, solved with machine learning
recognise and be able to apply major algorithms to solve stated problems
understand and be able to reproduce core machine learning algorithms
understand dependencies between algorithms, their advantages and disadvantages
be able to use data analysis libraries from python - numpy, scipy, pandas, matplotlib and scikit-learn
understand, which kinds of algorithms are more appropriate for what kinds of data
know, how to transform data to make it more suitable for machine learning algorithms

Expected Learning Outcomes

To name basic components of the binary tree
Can derive algorithm in general and particular loss-function cases, see conections
Can derive continual learning update
Can derive CUMSUM algorithm
Can derive updates for kmeans
Can state and explain one-class SVM optimization problem
Can state optimization problem for spectral clustering
To be able to derive and analyse closed form solution from scratch
To be able to explain why error rate cannot be used in gradient training of a classifier
To be able to formulate PCA as probabilistic model
To be able to formulate PCA as sequential optimization problem
To be able to make derivations for GMM
To be able to show solution of PCA given SVD decomposition
To be able to write pseudo-code for gradient decent algorithm
To be familiar with extension of model to GLM (Poisson, Logistic), multioutput
To be familiar with key objects of the course
To define formally problem of binary classification
To derive impurity criterions for regression and classification problems
To derive statistical results for bootstrap and ml algorithms in simple cases
To explain the properties of different quality metrics
To formulate and statistical concepts: bootstrap, bias, variance
To formulate optimization objective for Ligostic Regression and SVM
To get familiar with anomaly detection based on convex-hull methods and disroder testing
To get familiar with EM algorithm
To get familiar with variety of approaches to clustering: metric-based, graph-based and hierarchical
To get idea of functional gradient and its projection
To know how tree can be applied to unsupervisied problems
To know pruning and regularization strategies
To understand and can derive properties of l1, l2 regularization
To understand basic idea of proximal updates in context of machine learning problems
To understand bias-variance tradeoff in machine learning tasks
To understand computational complexity of kernel methods
To understand concept of overfitting and crossvalidation, can derive LOOCV
To understand connection of course contents with applications
To understand connection of machine learning algorithms and statistical methods such a bootstrap
To understand course goals
To understand foundation of gradient approaches and the role of each component (learning rate, precondition)
To understand general idea of log reg
To understand geometric interpretation of linear classifier and corresponding notation
To understand implementation tricks
To understand reglarization techniques
To understand what is a kernel and kernel trick
To understand what is a support vector
To write psedocode for greedy tree construction

Course Contents

Introduction to Machine Learning
Basic gradient optimization.
Linear Regression Model
Linear Classification
Logistic Regression and SVM
Decision Trees
Bagging, Random Forest and Bias-Variance Tradeoff
Gradient boosting
Clustering and Anomaly Detection
EM and PCA
Bayesian Linear Regression
GP for regression and classification tasks
MLP and DNN for Classification
Deep Generative Models

Assessment Elements

Домашние задания
ДЗ — средняя оценка за все домашние задания
Экзамен

Interim Assessment

2021/2022 2nd module
0.7 * Домашние задания + 0.3 * Экзамен

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. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008

Recommended Additional Bibliography

Mehryar Mohri, Afshin Rostamizadeh, & Ameet Talwalkar. (2018). Foundations of Machine Learning, Second Edition. The MIT Press.

Бакалаврская программа «Программная инженерия»

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Адрес

Mathematical Methods for Data Analysis