Бакалавриат
2026/2027
Машинное и глубинное обучение
Статус:
Курс по выбору (Международная программа по экономике и финансам)
Кто читает:
Международный институт экономики и финансов
Где читается:
Международный институт экономики и финансов
Когда читается:
4-й курс, 1-4 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Лагиос Николас
Язык:
английский
Кредиты:
10
Контактные часы:
112
Course Syllabus
Abstract
This course covers the core methods of machine learning, with applications to economics and statistics.
Topics include supervised and unsupervised learning: linear and logistic regression, K-nearest neighbors,
decision trees, random forests, boosting, support vector machines, regularized regression, clustering,
and dimensionality reduction. The course also introduces the fundamentals of neural networks and
causal machine learning, including double machine learning and causal forests. Students implement
methods in Python/R/Stata, present a research paper and complete an original project. This course covers Semester 1
(September to January); deep learning is covered separately in Semester 2 (January to April). Pre
requisites: calculus, linear algebra, probability and statistics, and basic programming in 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
- Construct machine learning models on the proposed data sets in Python
- Evaluate performance of the models.
- Build features suitable for the selected machine learning models.
- Tune models to improve prediction and classification performance of the models.
- Construct machine learning models on the proposed data sets in Statistical Software
- Apply causal machine learning methods, including double machine learning and causal forests, to estimate treatment effects in high-dimensional settings
- Critically evaluate applied machine learning research, including the research question, the identification strategy, and the contribution of machine learning
Course Contents
- Intro to Statistical learning
- Linear Regression (SLR) and K-Nearest Neighbors (KNN)
- Classification: 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, and Hierarchical Clustering
- Causal Machine Learning
- Introduction to Neural Networks and Deep Learning
- 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
- Midterm Test semester 1These are individualized tests. The assessment of the test is based on the marking scheme that comes with the test assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 10, which is the maximum grade for the test on the 10 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. The test is conducted with the help of Safe Exam Browser.
- Quizzes (semester 1)The grade for the current category is calculated as cumulative from the beginning of the course. Quizzes are conducted with the help of Safe Exam Browser.
- Home Assignments (semester 1)Home assignments. The grade for the current category is calculated as cumulative from the beginning of the course.
- Final Test (semester 1)This is the individualized test. In general, expect 60 questions, 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 test assignment. Each problem and their sub parts are worth a certain number of points, the sum of these points is equal to 10, which is the maximum grade for the test on the 10 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. The test is conducted with the help of Safe Exam Browser.
- Home Assignment (semster 2)The home assignment is a low-stakes preparation task for the theory exam and in-class hackathon. Its purpose is to help students practice the deep learning workflow before the exam.
- In-Class Hackathon (semester 2)The in-class hackathon is a practical assessment of students’ ability to implement a deep learning workflow independently. Students will build, train, evaluate, and improve a neural network model on a given task within the allocated time.
- ExamThe theory exam assesses students’ conceptual understanding of deep learning. It focuses on whether students can explain and reason about neural networks, training behavior, optimization, regularization, evaluation, and common modeling issues.
- Paper Replication, Presentation and Critical AnalysisEach team takes one applied machine learning paper from a list provided by the lecturer, reproduces a central result from it using the authors' public replication materials, presents the paper and the reproduced result in a lecture or seminar slot, and submits a short critical analysis of two to three pages. Where replication data are restricted, the team reconstructs the result on simulated or substitute data agreed with the lecturer.
- End Term ProjectTeams complete an original empirical machine learning project on a dataset of their choice: a one page proposal submitted in the first module, a written report of five to eight pages, and a final presentation held in the last teaching weeks of the second module. Weights within the category: proposal 0.05, written report 0.10, final presentation 0.05.
Interim Assessment
- 2026/2027 4th module0.1 * End Term Project + 0.05 * Home Assignments (semester 1) + 0.075 * Paper Replication, Presentation and Critical Analysis + 0.4 * Exam + 0.05 * Home Assignment (semster 2) + 0.05 * Quizzes (semester 1) + 0.1 * In-Class Hackathon (semester 2) + 0.075 * Midterm Test semester 1 + 0.1 * Final Test (semester 1)
