Бакалавриат
2021/2022
Машинное обучение
Статус:
Курс обязательный (Программа двух дипломов по экономике НИУ ВШЭ и Лондонского университета)
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:
24
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
10
Контактные часы:
112
Course Syllabus
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 apply R programming language and dive deeper into mathematical, statistical, and algorithmic concepts. 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 or R.
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) & 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
- homework assignments
- Midterm Exam sem 1
- Final exam
- Quizzes
- UoLUniversity of London Grade, which includes an exam and coursework.
- Participation
- Exam sem 1
Interim Assessment
- 2021/2022 2nd moduleYour current grade through module 2 is reported. For final grade see module 4.
- 2021/2022 4th module0.2 * Final exam + 0.2 * Quizzes + 0.1 * Participation + 0.3 * homework assignments + 0.1 * Midterm Exam sem 1 + 0.1 * Exam sem 1
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