ITF31519 Practical Machine Learning (Autumn 2021)
Facts about the course
- ECTS Credits:
- 10
- Responsible department:
- Faculty of Computer Science, Engineering and Economics
- Campus:
- Halden
- Course Leaders:
-
- Sukalpa Chanda
- Kazi Shah Nawaz Ripon
- Teaching language:
- Norwegian or English
- Duration:
- ½ year
The course is connected to the following study programs
This course is compulsory in
-
Bachelor in Computer Science – specialisation in machine learning
Elective course for others.
Absolute requirements
ITF10619 Programmering 2
Recommended requirements
ITD20218 Statistikk og statistisk programmering and ITD15020 Kalkulus (or taken in parallel with this course)
Lecture Semester
5th semester (Autumn).
The student's learning outcomes after completing the course
Knowledge
The student
-
understands what a machine learning problem is, how to solve it and ethical challenges related to it
-
knows the workflow used in machine learning
Skills
-
The student can use platforms and packages for machine learning
General competence
-
The student can program machines so that they can learn to solve problems on their own
Content
Application of
-
different techniques for machine learning
-
methods for evaluating models for machine learning
Forms of teaching and learning
Lectures, project work and lab-supervision.
Workload
Approx. 250 hours.
Coursework requirements - conditions for taking the exam
Up to 4 mandatory exercises.
The coursework requirements must be approved before the student can take the exam
Examination
Individual portfolio assignment
The portfolio will include the following:
-
a comparison of models on a selected problem
-
a report describing the project/program
The portfolio is assessed as a whole. Grading scale from A to F.
Examiners
External and internal examiner, or two internal examiners.
Conditions for resit/rescheduled exams
Upon re-examination, all parts of the portfolio must be retaken.
Course evaluation
This course is evaluated by a:
-
Mid-term evaluation (compulsory)
The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.
Literature
Last updated 03.06.2020.
The reading list may be subject to changes before 1st of June 2021.
Online resources that are made available on the university college's learning platform
Support literature:
Oliver Theobald: Machine Learning for Absolute Beginners: A Plain English Introduction (Second Edition).