Bachelor
2024/2025
Machine Learning Systems Design
Type:
Elective course (Applied Mathematics and Information Science)
Delivered by:
Big Data and Information Retrieval School
When:
4 year, 3 module
Online hours:
20
Open to:
students of one campus
Language:
English
Course Syllabus
Abstract
During the course, students will be able to form an idea of how ML systems work in the real world.: Together, we will trace the lifecycle of an ML task from requesting a business and/or product to product launch, use, and monitoring.
The purpose of the course is to learn how to see the entire task, find additional impact, and iteratively develop a project from a simple baseline to an optimal solution.
Learning Objectives
- The aim of the course is to teach students the basics of designing machine learning systems and successfully apply the acquired skills in practice.
Expected Learning Outcomes
- To understand the fundamentals of machine learning system design
- To know how to apply a business statement
Course Contents
- ML in a digital product
- Business statement and formalization of the task
- Working with data
- A/B tests
- Infrastructure and pipelines in ML
- Neural networks
Bibliography
Recommended Core Bibliography
- Rogers, S., & Girolami, M. (2016). A First Course in Machine Learning (Vol. 2nd ed). Milton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1399490
Recommended Additional Bibliography
- Miroslav Kubat. An Introduction to Machine Learning. Springer, 2015 (296 pages) ISBN: 9783319200095: — Текст электронны // ЭБС books24x7 — https://library.books24x7.com/toc.aspx?bookid=117295