Кто читает:: Департамент образовательных программ

Статус:: Курс по выбору

Когда читается:: 2-й курс, 1 модуль

Преподаватель

Мартинез Саито Марио

Full Syllabus

Abstract

Prerequisites: Basic knowledge of Statistics and Python. This course aims to provide state-of-the-art techniques of mathematical statistics as well as new and modern methods of machine learning. What is more important, this course will focus on practical activities and it will allow students to learn on their mistakes. Moreover, skills and knowledge obtained during this course could be applied to almost any field of science and industry. Students will know statements of all major machine learning problems and mathematical details of the most important data analysis methods and algorithms. Also, they will obtain skills, such selection of an appropriate method for solving particular data analysis problems, performance of basic data processing and visual analysis, features generation for subsequent machine learning, application machine learning libraries, algorithm's selection hyperparameters, critically evaluate the obtained results and redesign data-processing pipelines, ability to solve real-world data science problems using modern machine learning techniques.

Learning Objectives

Students will get familiar with the basic concepts and methods of classical machine learning, and will acquire working knowledge of how to select and apply machine learning techniques to solve real world problems.

Expected Learning Outcomes

Getting acquainted with the methods of model-free reinforcement learning, such as value learning.
Recognizing and applying the most popular types of decision trees.
Understanding how backpropagation works in neural networks with multiple layers.
Deliver a presentation on a topic related to machine learning
Understanding the basics of model-based reinforcement learning and its application to complex problems
Applying machine learning methods to practical tasks such as data and text processing
Learning how to configure neural nextworks for multiclass recognition and how to interpret patterns on high-dimensional spaces through dimensionality reduction techniques
Students can prepare data for analysis
Students can make tables with summary statistics, static and dynamic graphs
Students can make regression analysis
Students can apply basic classification algorithms
Students can apply ensemble models for classification or regression tasks
Students can apply basic techniques of dimensionality reduction
Students can make data clustering

Course Contents

Data preprocessing
EDA. Data visualization
Regression
Classification
Ensembles
Dimensionality reduction
Cluster analysis

Assessment Elements

History, latest advances, development frameworks, coding project
Exam
Participation
Active participation during the seminars

Interim Assessment

2023/2024 1st module
0.4 * Exam + 0.3 * History, latest advances, development frameworks, coding project + 0.3 * Participation

Bibliography

Recommended Core Bibliography

Pattern recognition and machine learning, Bishop, C. M., 2006

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

MARTINEZ-SAITO MARIO -
SHALOM KSENIYA VLADIMIROVNA

Магистерская программа «Обучение и оценивание как наука»

Machine Learning II