DS413
Industrial Machine Learning
Faculty
Emeli Dral
Chief Technical Officer & Co-founder at Evidently AI
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
The module covers topics related to industrial applications of machine learning. Nowadays machine learning technologies are widely used in practice in various applied fields such as retail, mass media, PR and marketing, banking, telecommunications, manufacturing, science and many others. It is very important to use the appropriate methods in every project, but often the choice of a particular machine learning algorithm does not play a key role. Often the most important factors are the appropriate formulation of the problem from the business point of view, the correct mathematical formalization of the problem, an accurate assessment of the potential economic effect.
In the course, we will learn the structure and the lifecycle of the machine learning-based project and cover topics ranging from the problem statement definition to the final model quality assessment, as well as an estimation of the economic effect.
Learning highlights
- Identify cases where machine learning techniques should be applied
- Apply machine learning algorithms and techniques to the real-world applications
- Formulate problem statement and quality criteria
- Estimate the potential economic effect of the machine learning models
- Develop a demo stand for ML-based application
Course outline
15 classes
Course Introduction
Introduction into industrial data analysis
Preliminary project phase
- Math problem statement versus Business goal
Preliminary project phase
- Data sample analysis and request
Preliminary project phase
- Economic effect estimation
Preliminary project phase
- Project team
Project work phase
- EDA and data visualization for car insurance
Project work phase
- EDA and data visualization: user segmentation
Project work phase
- ML-based service development
Case study
- Gold mining
Case study
- Demand forecasting (the case may be replaced by another one)
Offline validation
- Model quality assessment
Online validation
- AB-testing technique
Session 13
Common mistakes in machine learning projects
Final Exam
Final Exam
Demonstration of Projects
Demonstration of Projects
Course materials
Books
Prerequisites
General IT background
Programming Python
Python for Data Analysis (Pandas, Numpy, Scipy, Sklearn)
The Probability Theory and Mathematical Statistics
Time Series Analysis (basics)
Machine Learning (at least an introductory course)
Probability and Statistics: Theory and Implementation
Nov 30 - Dec 18, 2020
Andrey Khokhlov
Chief Researcher, IEPT RAS
Introduction to Machine Learning
Feb 01 - Feb 19, 2021
Iurii Efimov
Senior Researcher at Artec 3D
Ivan Provilkov
Head of Machine Learning at STAI
Nikolay Karpachev
Machine Learning Developer at Yandex
Intro to Programming 3: Python
Apr 12 - Apr 30, 2021
Hossein Yousefi
Co-founder and CTO at Identi
Methodology
Each three-hour session will consist of a lecture and a seminar. During the course, students will learn basic concepts about applying machine learning algorithms and techniques to industrial problems, such as churn prediction and prevention, demand prediction, recommender system developments, etc. In seminars, students will work on the problem statement, design of experiments and quality assessment, model implementation and its economic effect estimation. Also during the course students will try their hands at demo stand development in teams.
Grading
Emeli Dral is a Co-founder and Chief Technology Officer at Evidently AI, a startup developing tools to analyse and monitor the performance of machine learning models.
Prior to that, she co-founded a startup focused on the application of machine learning in the industrial sector, and served as the Chief Data Scientist at Yandex Data Factory. She led a team of accomplished data scientists and oversaw the development of machine learning solutions for various industries - from banking to manufacturing. Emeli is a lecturer at the Yandex School of Data Analysis and Harbour.Space University, where she teaches courses on machine learning and data analysis tools. In addition, she is a co-author of the Machine Learning and Data Analysis curriculum at Coursera. In 2017, she co-founded Data Mining in Action, the largest open data science course in Russia with over 500 students in each batch.
See full profileApply for this course
Industrial Machine Learning
by Emeli Dral
Total hours
45 Hours
Dates
Jul 12 - Jul 30, 2021
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.