2023/2024

Modern Decision Making: Applied Machine Learning

Type: Mago-Lego

Delivered by: Big Data and Information Retrieval School

When: 1 module

Online hours: 52

Open to: students of one campus

Instructors: Maria Tikhonova

Language: English

ECTS credits: 3

Contact hours: 10

Course Syllabus

Abstract

Machine learning is a very popular and important field in the modern world. Every day lots of people deal with products that have been created using various machine learning technologies. In order to enable the machines to make right decisions based on data, different problems need to be addressed. The course is focused on the advanced tasks and instruments of data analysis and machine learning. The course is aimed at the participants who liked the basics of data analysis and machine learning and who want to study data science in more detail. Practice parts are conducted in programming language Python and are based on different libraries such as numpy, pandas, matplotlib, scikit-learn, and others. In order to successfully pass the course, listeners are required to have mathematical skills at the school level, skills of programming in Python, and also a basic knowledge of data analysis and machine learning. Listener's performance is evaluated using programming assignments, theoretical tests based on the materials from lectures, and project assignment.

Learning Objectives

• After the end of the course, the listeners will: - learn how to process categorical data; - examine the details of different boosting methods and learn how to construct multi-level models using such ensembling techniques as blending and stacking; - know how to validate and interpret machine learning models; - be able to solve tasks with highly imbalanced datasets; - master different techniques of clustering, dimensionality reduction and data visualization; - gain skills in working with recommender systems.

Expected Learning Outcomes

• Learn how to process categorical data.
• Examine the details of different boosting methods and learn how to construct multi-level models using such ensembling techniques as blending and stacking.
• Know how to validate and interpret machine learning models.
• Be able to solve tasks with highly imbalanced datasets.
• Master different techniques of clustering, dimensionality reduction and data visualization.
• Gain skills in working with recommender systems.

Course Contents

• 1. Handling categorical data
• 2. Advanced ensembling techniques
• 3. Model verification
• 4. Handling imbalanced data
• 5. Recommender Systems
• 6. Clustering & Visualization

Assessment Elements

• Quizzes
  Weekly quizzes
• Programming Assignments
  Weekly programming assignments.
• Final Project

Interim Assessment

• 2023/2024 1st module
  0.16 * Final Project + 0.42 * Programming Assignments + 0.42 * Quizzes

Bibliography

Recommended Core Bibliography

• Molnar, C. (2018). iml: An R package for Interpretable Machine Learning. https://doi.org/10.5281/zenodo.1299058

Recommended Additional Bibliography

• Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019
• Machine learning in action, Harrington, P., 2012