Introduction to Machine Learning
Syllabus, Bachelor's level, 1DL034
- Code
- 1DL034
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Computer Science G2F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 7 March 2019
- Responsible department
- Department of Information Technology
Entry requirements
60 credits of which at least 15 credits in mathematics including Probability and Statistics DV and Linear Algebra II, and 30 credits in computer science including a second course in programming and an introduction to scientific computing or numerical methods.
Learning outcomes
On completion of the course, the student should be able to:
- explain and compare basic machine learning methods;
- use machine learning software in practical applications;
- evaluate the applicability of the studied methods.
Content
This is a practical introduction to machine learning: its terminology, an overview of basic supervised and unsupervised methods (for example, regression, classification trees, an introduction to neural networks and deep learning, and clustering), use of established tools for machine learning and practical aspects such as dimensionality reduction and cross validation.
Instruction
Lectures, laboratory work and assignments.
Assessment
Written exam and oral and written assignments.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.