Machine Learning (5cr)
Code: 5G00DM02-3001
General information
- Enrolment period
- 10.06.2020 - 30.09.2020
- Registration for the implementation has ended.
- Timing
- 02.09.2020 - 09.12.2020
- Implementation has ended.
- Credits
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- ICT Engineering
- Campus
- TAMK Main Campus
- Teaching languages
- English
- Seats
- 0 - 40
- Degree programmes
- Bachelor's Degree Programme in Software Engineering
Objectives (course unit)
The student is able to create and use Machine Learning Algorithms in Python. The student learns how to make predictions and analysis and knows which Machine Learning model to choose for each type of problem.
Content (course unit)
Data Preprocessing, Machine Learning models like classification and regression, Python Libraries for Data Science.
Prerequisites (course unit)
Big Data Systems, Basic knowledge of programming
Assessment criteria, satisfactory (1-2) (course unit)
Student is able to conduct data Pre-processing for different datasets with guidance. Student knows about machine Learning models like classification and regression. Student is able to exploit some Python Libraries for Data Science with guidance.
Assessment criteria, good (3-4) (course unit)
Student is able to conduct data Pre-processing for different datasets. Student knows and understands machine Learning models like classification and regression. Student is able to exploit Python Libraries for Data Science.
Assessment criteria, excellent (5) (course unit)
Student is able to independently conduct diversely data Pre-processing for various datasets. Student knows and understands in depth machine Learning models like classification and regression. Student is able to exploit diversely Python Libraries for Data Science in various situations.
Exam schedules
No exam.
Assessment methods and criteria
The grade of the course consists of both exercises and practical work (max 50 p). The requirements for the practical will come to Moodle during the course.
The grades are based on the table below:
0 0
1 12
2 22
3 30
4 38
5 46
Weekly exercises can bring in 10 extra points (1 point / week assingment, you tried to solve all the tasks).
Assessment scale
0-5
Teaching methods
Teaching in teams. Links and materials on moodle.
Learning materials
Teaching in teams. Links and materials on moodle.
Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)
Practical work has not been returned to moodle