Study Guide

Objective

After completing the course the student:
- Can define the main concepts related to data analytics and machine learning
- Understands the value and the drivers for data analytics and machine learning
- Can describe the processes of data analytics and machine learning
- Can use some tools for data analytics and machine learning

Content

Introduction to data analytics and machine learning
Data analytics process and methods
Machine learning process and methods
Practical work

Materials

Course book:

Aurélien Géron.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
2nd Edition.
Publisher : O'Reilly Media; 2nd edition
(October 15, 2019)

We read chapters 1-10 of the book of menus. They have about 300 pages, but some are skipped over.

The course book can be read in electronic form from our institution's eBook Central database.

The course also has reading material, which will be announced during the course.

International connections

The course includes approximately 12 guided working and theory sessions, 9 personal practice tasks and group work.
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Group work is done in groups of 3-4 people outside of guidance sessions. The group sets aside 15 minutes outside of guidance sessions to present the group work.

Content scheduling

Introduction to machine learning:
- data exploration
- data processing and preparation
- model training, selection, and evaluation
- taking the model into production
- supervised learning
- unsupervised learning
- visualization

We proceed in general according to the chapters in the course book.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.
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In order to receive an approved performance, the student must receive an acceptable mark for both 1) personal practice tasks and 2) group work.
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You can get at least 10 points for each practice task. You can therefore get a maximum of 90 points from all 9 practice tasks.
Personal practice tasks: 25 points -> grade 0.5; 38 -> 1.0; 50 -> 1.5; 63 -> 2.0; 75 - 2.5; 88 -> 3.0. The tasks are checked in the demos. Must be present at the demo sessions.
Teamwork final project: 0.0 - 2.0.
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(In both cases, 0.5 is the first accepted grade)