Математические методы анализа данных

Бакалавриат 2020/2021

Статус: Курс обязательный (Программная инженерия)

Направление: 09.03.04. Программная инженерия

Кто читает: Департамент больших данных и информационного поиска

Где читается: Факультет компьютерных наук

Когда читается: 3-й курс, 3, 4 модуль

Формат изучения: без онлайн-курса

Преподаватели: Карпов Максим Евгеньевич, Кохтев Вадим Михайлович, Кузина Анна Вадимовна, Полунина Полина Алексеевна, Тихонова Мария Ивановна

Язык: английский

Кредиты: 5

Контактные часы: 60

Full Syllabus Ask Question

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

Course Contents

Introduction to Machine Learning
problem set-up, problem types, features, examples of application in industry
Basic gradient optimization.
Running example: Linear Regression - Vanilla gradient descent - Learning rate and pre-conditioning - Stochastic version - Proximal updates
Linear Regression Model
- Close-form solution analysis and connection to MLE - Regularization and affection of closed-form solution (in orthogonal feaures case) - Regularizatin and Overfitting, basic cross-validaton - Iterative solution and stochastic version - Continual update and efficient LOOCV (Sherman-Morison update, QR) - Outlier diagnostic
Linear Classification
- problem formulation - margin of a classifer - error rate loss function and its upper bounds - quality metrics for classification (Pr, Recall, F1, ROC-AUC, etc.) - Breifly define Logistic Regression
Logistic Regression and SVM
- Logistic Regression as GLM - Connection to MLE - SVM in Linearly separable and non-separable cases - Connection to distance between convex sets - Support vectors analysis - Kernel trick
Decision Trees
- Define binary tree (nodes, predicates, leaves) - Define Impurity Criterion - Greedy algoritm for tree construction - Pruning strategies - Regularization strategies - Decision trees for unsupervised problems and anomaly detection
Bagging, Random Forest and Bias-Variance Tradeoff
Bias-Variance Tradeoff: - Introduce concept, remind regularization - Introduce bootstrap, show on simple statistics, introduce jack-knife - Introduce bagging in machine learning algorithms Random Forest - introduce the algorithm - put in statistical context: bias-variance, margin, limit prediction distribution
Gradient boosting
- Introduce general boosting algorithm - Show particular cases for regression and classification ie Adaboost, "weight" of samples interpretation - Discuss implementation details: regularization (step size, noise, step-back), second order optimization, stochastic versions - Stacking
Clustering and Anomaly Detection
Clustering - kMeans - spectral clustering - hierarchical clustering Anomaly Detection - One class SVM - CUMSUM
EM and PCA
- genral formulation of EM algorithm, GMM mixture - connection with kmeans - formulation of PCA as optimization problem - solution with SVD - formulaation of PCA as probabilistic model - solution with EM algorithm
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

Interim assessment (4 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.

Course Syllabus