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

Mainly according to the book (from chapters 1-10)
[Aurélien Géron] Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Publisher : O'Reilly Media; 2022, 3rd Edition

In addition: Material prepared by the teacher, online material and tasks in the learning environment.

International connections

The course includes approximately 12 guided working and theory sessions, 9 personal practice tasks and group work.
*
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.

Student workload

Contact hours:
- Course start-up: 2h
- Weeks 3 - 5: Theory & practice (3h/week): 5 x 3h = 15h
- Weeks 9 - 15: Theory & practice (3h/week): 7 x 3h = 21h
- Week 16: Project work presentations: 3h
- In addition, in weeks 4 - 15, about 10 support and inquiry hours: 10 x 1h = 10h

Total contact hours: about 51 hours

Independent study and homework: about 90 h

Content scheduling

Week 2:
- Machine learning landscape (Introduction to machine learning)
Weeks 3 - 7:
- End-to-end machine learning process
Weeks 9-15:
- Presentation of teamwork
- Classification
- Training linear models
- Decision trees
- Unsupervised learning
- Neural networks (Introduction to neural networks)
Week 16: Presentation of project works

Further information

The course materials and assignments are at ITS.
The exercises are mainly performed using Jupyter Notebook.
Communication about the course via ITS, but also via the course's Teams channel.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.

You get points from the exercises n the contact class and homework, which affect the evaluation by 3 units.
About half of the exercises are done in contact classes.

The project work also affects the evaluation by 2 units. The project work is graded in ITS from 0 to 5, which is influenced by both the teacher's assessment and the peer assessment given by the rest of the project team.

The course can only be passed by doing both practice tasks and participating in project work.

Assessment criteria, fail (0)

The student does NOT participate in the project work or gets the project work grade 0 (Failed) OR did not get at least 40% of the points in the course exercises.

Assessment criteria, satisfactory (1-2)

The student got 40-59% of the points for the exercises in the course AND got a grade of 1 - 3 for the project work.

Assessment criteria, good (3-4)

The student got 60-84% of the points for the exercises in the course AND got a grade of 3 - 4 for the project work.

Assessment criteria, excellent (5)

The student got at least 85% of the points for the exercises in the course AND got a grade 5 for the project work.

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

Evaluation scale

H-5

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.
*
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.
*
In order to receive an approved performance, the student must receive an acceptable mark for both 1) personal practice tasks and 2) group work.
*
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.
*
(In both cases, 0.5 is the first accepted grade)

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

Evaluation scale

H-5