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Studies
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The Institute
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DS413

Industrial Machine Learning

Barcelona Campus
Jul 08, 2024 - Jul 26, 2024
By the end of this course, students will be able to understand the structure and lifecycle of a machine learning-based project, and develop a demo stand for a ML-based application.
Barcelona Campus
Jul 08, 2024 - Jul 26, 2024
Emeli Dral

Faculty

Emeli Dral

Chief Technical Officer & Co-founder at Evidently AI

Course length

3 weeks

Duration

3 hours
per day

Total hours

45 hours

Credits

6 ECTS

Language

English

Course type

Offline

Fee for single course

€1500

Fee for degree students

€750

Skills you’ll learn

Data AnalysisMachine LearningMathematical ModelingProblem Statement in Data ScienceML Model Quality EstimationPotential Economical Effect EstimationMonitoring System for ML-based ServiceDemo Stand for ML-based Service
OverviewCourse outlineCourse materialsPrerequisitesMethod & grading

Overview

The module covers topics related to industrial applications of machine learning. Nowadays, machine learning technologies are widely used in various fields such as retail, mass media, PR and marketing, banking, telecommunications, manufacturing, science and many others. Even though it is very important to use the appropriate methods in every project, often the choice of a particular machine learning algorithm does not play a key role. Most of the time, the most important factors are the appropriate formulation of the problem from the business point of view, the correct mathematical formalization of the problem, and 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

Dive into the details of the course and get a sense of what each class will cover.
Monday
Tuesday
Wednesday
Thursday
Friday
Monday
1

Course Introduction

Introduction into industrial machine learning.

Tuesday
2

Session 2

  • Business goal vs Math problem statement.
Wednesday
3

Session 3

  • Economic effect estimation.
  • Data sample analysis and request .
Thursday
4

Session 4

  • Acceptance results of the model.
Friday
5

Session 5

  • Project work: plan, team, timeline.
Monday
6

Session 6

  • Model validation.
Tuesday
7

Session 7

  • Case study: discounts optimization.
Wednesday
8

Session 8

  • Experiment management: ML flow.
Thursday
9

Session 9

  • Workflow management: prefect, airflow.
Friday
10

Session 10

  • Demo service development: streamlit
Monday
11

Session 11

  • Online validation: AB-testing.
Tuesday
12

Session 12

  • Additional topics.
Wednesday
13

Session 13

Batch & online ML model monitoring.

Thursday
14

Session 14

Online validation: additional topics; Final test.

Friday
15

Session 15

Projects demonstration.

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)

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, and model implementation and its economic effect estimation. Also, during the course students will develop a demo stand in teams.

Grading

The final grade will be composed of the following criteria:
20% - Assignment 1 (problem statement)
20% - Assignment 2 (quality estimation & experiment design)
20% - Final Test
40% - Course Project (ml-based demo service development)
-
Emeli Dral

Faculty

Emeli Dral

Chief Technical Officer & Co-founder at Evidently AI

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 profile

Apply for this course

Snap up your chance to enroll before all spaces fill up.

Industrial Machine Learning

by Emeli Dral

Total hours

45 Hours

Dates

Jul 08 - Jul 26, 2024

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.