Basics of Machine Learning and Classification in Python (3cr)
Code: 5G00FC54-3001
General information
- Enrolment period
- 27.05.2019 - 30.09.2019
- Registration for the implementation has ended.
- Timing
- 26.08.2019 - 22.12.2019
- Implementation has ended.
- Credits
- 3 cr
- RDI portion
- 2 cr
- Mode of delivery
- Contact learning
- Campus
- TAMK Main Campus
- Teaching languages
- Finnish
- Degree programmes
- Degree Programme in ICT Engineering, students who began in 2014-2018
Objectives (course unit)
The aim is to provide a tool package ("a set of algorithms") to be able to solve simple real world problems with machine learning.
The course time load will averagely be: 20 % in-class lectures and 80 % of student own/pair working.
Content (course unit)
Timetable:
Lecture 1. 9.10. Introduction. Basic of machine learning and classification. Introducing the course assignments. Homework.
16.10. Autumn brake
Lecture 2. 30.10. Decision Trees and Random Forests. Homework.
Lecture 3. 13.11. Support Vector Machines. Homework.
Lecture 4. 27.11. Ensemble Learning, Bagging and Boosting. Homework
Lecure 5. 11.12. Exam.
Prerequisites (course unit)
Knowledge and skills of basics of programming and engineering mathematics.
Assessment criteria, satisfactory (1-2) (course unit)
Evaluation/grading criteria:
40 % from course exercises
40 % from course assignment
20 % from multiple choice exam
A student with the best course assignment will get the final grade + 1.
The student must complete at least one course exercise. Grades from course exercises:
1 working exercise = grade 2
2 working exercise = grade 3
3 working exercise = grade 4
4 working exercise = grade 5
Assessment criteria, good (3-4) (course unit)
Evaluation/grading criteria:
40 % from course exercises
40 % from course assignment
20 % from multiple choice exam
A student with the best course assignment will get the final grade + 1.
The student must complete at least one course exercise. Grades from course exercises:
1 working exercise = grade 2
2 working exercise = grade 3
3 working exercise = grade 4
4 working exercise = grade 5
Assessment criteria, excellent (5) (course unit)
Evaluation/grading criteria:
40 % from course exercises
40 % from course assignment
20 % from multiple choice exam
A student with the best course assignment will get the final grade + 1.
The student must complete at least one course exercise. Grades from course exercises:
1 working exercise = grade 2
2 working exercise = grade 3
3 working exercise = grade 4
4 working exercise = grade 5
Location and time
Ensimmäinen kerta 9.10.2019 !!!!!
16.10 on syysloma.
Assessment scale
0-5