Study Guide

Objective

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

Content

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 study chapters 1, 2, 3, 4, 6, 9, and 10 of the book. 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.

Exam schedules

No Exam.

International connections

The course includes approximately 13 guided working and theory sessions, 9 personal practice tasks and a group work final project.
*
Final project is done in groups of 3-4 people outside of guidance sessions. The group sets aside 15 minutes to present the group work during the last session.

Student workload

Contact hours (approximately):
- One introductionary session: 2h
- 13 times 3h theory and practice: 13 x 3h = 39 hours
- Final projects and presentations: 24 hours

Home work: approximately 70 hours

Total: approximately: 135 hours

Content scheduling

- Week 03: Course Introduction
- Week 04: Landscape of machine learning
- Week 05: Data exploration
- Week 06: Data preparation
- Week 07: Model training, selection, and evaluation
- Week 08: Winter break - Visualization (self-study)
- Week 09: Demonstrations of Exercises 1 – 4
- Week 10: Classification
- Week 11: Training models
- Week 12: Decision trees
- Week 13: Unsupervised learning
- Week 14: Guidance to team work
- Week 15: Introduction to Neural networks
- Week 16: Demonstrations of Exercises 5 – 9
- Week 17: Team work presentations


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

Further information

Qualifications:

- Python programming skills and skills in utilizing Pandas for data manipulation and Numpy for numerical operations and array handling are required.
- Basic knowledge of probability, statistics and linear algebra is also beneficial.

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 (exercises) and 2) teamwork project.
- You get points from the 9 homework exercises, which affect the evaluation by 3 units.
- Occasionally, there may be classwork exercises, but their points are mostly optional and are considered as bonuses.
- 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 personal practice tasks and participating in teamwork project.

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.