Data Analytics and Machine Learning (5 cr)

Evaluation scale

H-5

Content scheduling

+ The course includes approximately 14 guided working and theory sessions, 9 personal homework assignments, 8-9 classwork assignment and a teamwork project

+ Final project is done in groups of 2-3 people outside of guidance sessions. The group sets aside 15 minutes to present the group work during the last session.

+ Content scheduling (pre-planning)
week 03: Course Kick-off: Overview of the course objectives, expectations, and structure. 
Week 04: Landscape of machine learning 
Week 05: Data Wrangling and Visualization using Pandas, Matplotlib, and Seaborn
Week 06: Data exploration (Chapter 2)
Week 07: Data preparation (Chapter 2)
Week 08: Winter break - Visualization (self-study)
Week 09: Model training, selection, and evaluation 
Week 10: Demonstrations of Exercises 1 – 4 
Week 11: Classification
Week 12: Training models
Week 13: Decision trees
Week 14: Guidance to team work
Week 15: Introduction to Neural networks 
Week 16: Demonstrations of Exercises 5 – 9 
Week 17: Team work presentations

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
3rd Edition.
Publisher : O'Reilly Media;
(2023)

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.

Teaching methods

- Participating in lectures (theory and practice)
- Learning through hands-on programming (classwork assignments)
- Completing homework assignments
- Interacting with the teacher and classmates
- Enhancing knowledge through teamwork projects
- Following the flipped-classroom model (pre-session self-study of theoretical concepts followed by in-class practical application)

Exam schedules

+ No exam, retake not possible after the publication of the final assessment/course grade

International connections

- The course includes approximately 14 theory and practice sessions, where students engage with practical tasks.
- Homework exercises will be assigned, with some parts demonstrated during contact sessions.
- A teamwork project will be introduced in the second month, requiring students to apply their teamwork skills and the knowledge gained from the course to implement their final project.
- A flipped-classroom model may be used for some lectures, where students study the theoretical content at home and focus on practical implementation and discussions during class.

Completion alternatives

The practice works and exercises are mainly performed using Python and Jupyter Notebook.

Student workload

+ Student Responsibilities:
1. Class Participation and Assignments:
- Active participation in all classes, including the completion of in-class assignments, which must be submitted during class hours.
2. Homework Assignments:
- Completing 8-10 individual homework assignments, partially demonstrated during contact sessions. The exact number of the assignments will be announced at the first lecture)
3. Final Project:
- A group project (2-3 students) to be completed over Weeks 46 & 47, culminating in a presentation in Week 48.

+ 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 75 hours

Total: approximately: 140 hours

Further information

+ Qualifications/Prerequisites:
Student enrollment in the course will not be accepted by the instructor if they have not passed the following prerequisite courses:
- Python programming skills and skills in utilizing Pandas for data manipulation and Numpy for numerical operations and array handling
- Basic knowledge of probability, statistics and linear algebra

+ Communication Channel:
Itslearning and email