Industrial Machine Learning
Faculty Profiles
Evgeniy Riabenko
Senior Data Scientist at The Trade Desk, London
Emeli Dral
Chief Technical Officer & Co-founder at Evidently AI
Victor Kantor
Head of User Behaviour Analysis group at Yandex Data Factory
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 other areas. Using relevant techniques in each project is very important, but often selecting a particular machine learning algorithm does not play a key role. Frequently the most important factors include relevant problem statement in terms of business goals, correct mathematical formalization of the problem, precise estimation of the potential economic effect, criteria and metrics of decision quality estimation and other factors.
In the course, we will learn the structure and the lifecycle of the machine learning project and cover topics ranging from the problem statement to final quality assessment as well as estimation of the economic effect.
Learning highlights
- Identify cases where machine learning techniques should be applied
- Apply machine learning algorithms and techniques to real world applications
- Formulate problem statement and quality criteria
- Estimate potential economic effect of the machine learning models.
Course outline
5 classes
Session 1
Lifecycle of the typical machine learning project: from problem statement to quality evaluation.
Recommender systems in industries:
product and media recommendations.
Session 3
Hybrid recommender systems: mixing, switching, cascading and other techniques.
Session 4
Time Series Forecasting, main components and attributes. Autocorrelation and stationarity. ARIMA models: structure, properties, fitting, evaluation. Automatic forecasting approaches for single and multiple time series.
Applied machine learning cases:
churn prediction and prevention, product recommender system, sentiment analysis, etc.
Prerequisites
This course is one of three in a wholistic series.
Students that have already taken MSL-111 and those with prior experience with HTML, CSS, and Javascript building simple web pages will be good candidates for this module.
Evgeniy Riabenko is a data scientist and researcher with 15 years of experience in both industry and academia. He got his PhD in mathematical modelling from Moscow State University, and has expertise in statistics, machine learning, optimization, and bioinformatics.
He taught statistics and data analysis courses at Harbour Space, Moscow State University, Moscow Institute of Physics and Technology, Higher School of Economics, as well as on various online platforms.
See full profileEmeli 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 profileVictor Kantor was born in 1992, graduated with honors at MIPT — Moscow Institute of Physics and Technology, the Department of Data Analysis (the basic organization – Yandex).
Since 2011, Victor was engaged in data analysis and machine learning within various projects, and in 2012 he taught first as a teacher assistant, then held laboratory courses and subsequently lectured in data analysis courses.
See full profileApply for this course
Industrial Machine Learning
by Evgeniy Riabenko, Emeli Dral, Victor Kantor
Total hours
45 Hours
Dates
Apr 10 - Apr 28, 2017
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.