INDUSTRIAL MACHINE LEARNING
EMELI DRAL
We offer innovative university degrees taught in English by industry leaders from around the world, aimed at giving our students meaningful and creatively satisfying top-level professional futures. We think the future is bright if you make it so.
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.
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 statements in terms of business goals, correct mathematical formalisation 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.
Emeli Dral leads the data science team at Mechanica AI. She is responsible for the development of core products and technologies for the application of artificial intelligence in industrial processes.
Prior to co-founding Mechanica AI, she 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.
After completing this course, a student will be able to:
- 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
SKILLS:
-Data Science
-Machine Learning
-Artificial Intelligence
-System Architecture
ABOUT EMELI
HARBOUR.SPACE WHAT YOU WILL LEARN
DATE: 29 Jul - 16 Aug, 2019
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
HARBOUR.SPACE UNIVERSITY
RESERVE MY SPOT
DATE: 29 Jul - 16 Aug, 2019
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
All rights reserved. 2017
COURSE OUTLINESession 1
Introduction
Session 4
Machine learning model quality estimation
Session 3
Problem statement in data science
Session 2
Machine learning model lifecycle
INDUSTRIAL MACHINE LEARNING
BIBLIOGRAPHY
"The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani & Jerome Friedman (Springer, 2009)
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.
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 statements in terms of business goals, correct mathematical formalisation 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.
After completing this course, a student will be able to:
- 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
Emeli Dral leads the data science team at Mechanica AI. She is responsible for the development of core products and technologies for the application of artificial intelligence in industrial processes.
Prior to co-founding Mechanica AI, she 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.