Машинное обучение

Бакалавриат 2022/2023

Статус: Курс по выбору (Международная программа по экономике и финансам)

Направление: 38.03.01. Экономика

Кто читает: Международный институт экономики и финансов

Где читается: Международный институт экономики и финансов

Когда читается: 4-й курс, 1-4 модуль

Формат изучения: с онлайн-курсом

Онлайн-часы: 64

Охват аудитории: для своего кампуса

Преподаватели: Болдырев Алексей Сергеевич, Карпов Максим Евгеньевич, Лазарева Маргарита Владимировна

Язык: английский

Кредиты: 10

Контактные часы: 112

Full Syllabus Ask Question

Abstract

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods and several topics in deep learning, such as artificial neural networks, recurrent neural networks, convolutional neural networks, transformers and attention mechanisms, auto-encoders, etc. The first two modules (Sep-Dec) DSBA and ICEF students apply Python programming language and popular packages, such as pandas, scikit-learn and TensorFlow, to investigate and visualize datasets and develop machine learning models that solve theoretical and data-driven problems. The next two modules (Jan-Jun) DSBA/ICEF students dive deeper into mathematical, statistical, and algorithmic concepts and studying deep neural networks. During the entire period of study, students participate in Kaggle competitions in groups, a widespread format in data science, aiming at developing soft skills such as collaborative work, solving a research task, meeting the deadlines. Pre-requisites: at least one semester of calculus on a real line, vector calculus, linear algebra, probability and statistics, computer programming in high level language such as Python.

Learning Objectives

The course aims to help students develop an understanding of the process to learn from data, familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.

Expected Learning Outcomes

Build and interpret the data visualizations in Python and R programming language
Build features suitable for the selected machine learning models
Construct machine learning models on the proposed data sets in R
Evaluate performance of the models
Tune models to improve prediction and classification performance of the models

Course Contents

Math Essentials. Intro to Python in Google Colab
Intro to Statistical learning
Linear Regression (SLR) and K-Nearest Neighbors (KNN)
Classification with Logistic Regression, LDA, QDA, KNN
Resampling methods. CV, Bootstrap
Linear model selection & regularization
Non-linear regression
Decision Trees, Bagging, Random Forest, Boosting
Support Vector Machines/Classifiers
Clustering methods. PCA, k-Means, Hierarchical Clustering, DBSCAN
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) Networks
Transformer and Attention Layers
Reinforcement Learning

Assessment Elements

Home assignments
Home assignments. The grade for the current category is calculated as cumulative from the beginning of the course.
Tests
These are individualized, timed, (possibly) proctored and otherwise constrained tests to prevent cheating. In general, expect 60 questions in 60 minutes, some of which you may will have seen in quizzes. The assessment of the test is based on the marking scheme that comes with the exam assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 100, which is the maximum grade for the exam on the 100 point scale. The student is awarded the assigned number of points for the correct answer to each part of the question and partial credit may also be awarded. The grade for the current category is calculated as cumulative from the beginning of the course.
Quizzes
The grade for the current category is calculated as cumulative from the beginning of the course.
Participation
The grade for the current category is calculated as cumulative from the beginning of the course.
Exams
These are individualized, timed, (possibly) proctored and otherwise constrained tests to prevent cheating. In general, expect 60 questions in 60 minutes, some of which you may will have seen in quizzes. The assessment of the exam is based on the marking scheme that comes with the exam assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 100, which is the maximum grade for the exam on the 100 point scale. The student is awarded the assigned number of points for the correct answer to each part of the question and partial credit may also be awarded. The grade for the current category is calculated as cumulative from the beginning of the course.
Exams
These are individualized, timed, (possibly) proctored and otherwise constrained tests to prevent cheating. In general, expect 60 questions in 60 minutes, some of which you may will have seen in quizzes. The assessment of the exam is based on the marking scheme that comes with the exam assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 100, which is the maximum grade for the exam on the 100 point scale. The student is awarded the assigned number of points for the correct answer to each part of the question and partial credit may also be awarded. The grade for the current category is calculated as cumulative from the beginning of the course.

Interim Assessment

2022/2023 2nd module
0.1 * Participation + 0.3 * Home assignments + 0.2 * Exams + 0.2 * Quizzes + 0.2 * Tests
2022/2023 4th module
0.1 * Participation + 0.2 * Quizzes + 0.2 * Exams + 0.2 * Tests + 0.3 * Home assignments

Bibliography

Recommended Core Bibliography

Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286

Recommended Additional Bibliography

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

Authors

BOLDYREV ALEKSEY SERGEEVICH
KARPOV MAKSIM EVGENEVICH

Course Syllabus