Syllabus for Introduction to Machine Learning
Introduktion till maskininlärning
- 5 credits
- Course code: 1DL034
- Education cycle: First cycle
Main field(s) of study and in-depth level:
Computer Science G2F
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2019-03-07
- Established by:
- Revised: 2020-10-20
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2021
60 credits of which 15 credits in mathematics including Probability and Statistics DV and Linear Algebra and Geometry I, and 30 credits in computer science including a second course in programming and an introduction to scientific computing or numerical methods.
- Responsible department: Department of Information Technology
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.
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.
Lectures, laboratory work and assignments.
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.
- Latest syllabus (applies from Autumn 2021)
- Previous syllabus (applies from Autumn 2019)
Applies from: Autumn 2021
Some titles may be available electronically through the University library.