CM1001 Applied Machine Learning and Data Mining 7.5 credits
This course deals with how to process and draw conclusions of data through mining and machine learning. The course introduces some theory on machine learning, but focuses mainly on current applied methods.
Information per course offering
Information for Spring 2025 Start 14 Jan 2025 programme students
- Course location
KTH Flemingsberg
- Duration
- 14 Jan 2025 - 16 Mar 2025
- Periods
- P3 (7.5 hp)
- Pace of study
50%
- Application code
60586
- Form of study
Normal Daytime
- Language of instruction
Swedish
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
Contact
Jayanth Raghothama (jayanthr@kth.se)
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus CM1001 (Autumn 2020–)Content and learning outcomes
Course contents
Intended learning outcomes
Literature and preparations
Specific prerequisites
Följande slutförda kurser: HF1006, Linjär algebra och analys; HF1012, Matematisk statistik; HI1024 Programmering, grundkurs
Recommended prerequisites
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- LAB1 - Practial assignments, 5.0 credits, grading scale: A, B, C, D, E, FX, F
- RED1 - Accounting, 2.5 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.