Machine Learning II

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Educational Programmes

Course type:

Elective course

When:

2 year, 2 module

Instructor

Zakharov, Andrey

Course Syllabus

Abstract

Prerequisites: Basic knowledge of statistics and Python. This course introduces methods of classical machine learning. Students will get familiar with the major supervised and unsupervised machine learning tasks and algorithms. The course is practice oriented. Thus students will acquire practical skills in data preparation, visual analysis, training of the appropriate models, evaluation of their quality, and selection of hyperparameters. At the end of the course students are expected to become able to solve real-world research problems with the classical 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

• Students can prepare data for analysis
• 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
• Students can make tables with summary statistics and graphs

Course Contents

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

Assessment Elements

• EDA. Regression
  The homework assignment includes tasks related to exploratory data analysis and regression. Student presents the code and the output with the graphs, analysis estimates, interpretations etc. according to what is required in the tasks.
• Classification
  The homework assignment includes tasks related to classification. Student presents the code and the output with the graphs, analysis outputs, interpretations etc. according to what is required in the tasks.
• Unsupervised learning
  The homework assignment includes tasks related to unsupervised learning. Student presents the code and the output with the graphs, analysis outputs, interpretations etc. according to what is required in the tasks.
• Ensembles
  The homework assignment includes tasks related to work with ensembles. Student presents the code and the output with the graphs, analysis outputs, interpretations etc. according to what is required in the tasks.

Interim Assessment

• 2024/2025 2nd module
  0.25 * Classification + 0.25 * EDA. Regression + 0.25 * Ensembles + 0.25 * Unsupervised learning

Bibliography

Recommended Core Bibliography

• Aurélien Géron. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems: Vol. Second edition. O’Reilly Media.
• Chris Albon. (2018). Machine Learning with Python Cookbook : Practical Solutions From Preprocessing to Deep Learning: Vol. First edition. O’Reilly Media.
• Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python : A Guide for Data Scientists: Vol. First edition. Reilly - O’Reilly Media.
• Myatt, G. J., & Johnson, W. P. (2014). Making Sense of Data I : A Practical Guide to Exploratory Data Analysis and Data Mining (Vol. Second edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=809795

Recommended Additional Bibliography

• Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017

Authors

• ZAKHAROV ANDREY BORISOVICH