Valid for: 2024/25
Faculty: Faculty of Engineering LTH
Decided by: PLED F/Pi
Date of Decision: 2024-04-15
Effective: 2024-05-08
Main field: Machine Learning, Systems and Control
Depth of study relative to the degree requirements: Second cycle, in-depth level of the course cannot be classified
Elective mandatory for: MMSR1
Elective for: BME4, C4-pvt, C4-pvs, D4-bg, D4-mai, D4-se, E5, F4, F4-bs, F4-bg, F4-r, F4-mai, I4, L4-gi, Pi4-bam
Language of instruction: The course will be given in English on demand
The aim of the course is to establish a solid foundation in the principles and methods of machine learning, based on pertinent knowledge from mathematics, statistics, and optimization.
Additionally, the course seeks to provide insight into advanced techniques within modern machine learning.
Knowledge and understanding
For a passing grade the student must
Competences and skills
For a passing grade the student must
Judgement and approach
For a passing grade the student must
Grading scale: TH - (U, 3, 4, 5) - (Fail, Three, Four, Five)
Assessment: Compulsory assignments including computer work and written reports.
Approved results on these are enough to pass the course. To get a higher grade, the student also has to pass an oral examination.
For those who do not get all the assignments approved during the course there will be a chance to hand in improved versions during the following semester.
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Modules
Code: 0116. Name: Machine Learning.
Credits: 7.5. Grading scale: TH - (U, 3, 4, 5).
Assessment: For the grade 3 it is required to pass on all the assignments. For a higher grade one also has to study one or more individually assigned articles and be able to explain them sufficiently well at a seminar.
Admission requirements:
Assumed prior knowledge: FMAF05 Mathematics - Systems and Transforms or FMAF10 Applied Mathematics - Linear Systems, and one of the basic courses in Mathematical Statistics, e.g. FMSF45.
Course coordinator: Anders Holst,
studierektor@math.lth.se
Teacher: Mikael Nilsson,
mikael.nilsson@math.lth.se
Course administrator: Studerandeexpeditionen,
expedition@math.lth.se
Course homepage: https://canvas.education.lu.se/courses/20372