Course syllabus

Introduktion till maskininlärning, system och reglering
Introduction to Machine Learning, Systems and Control

FRTF25, 7,5 credits, G2 (First Cycle)

Valid for: 2021/22
Faculty: Faculty of Engineering, LTH
Decided by: PLED F/Pi
Date of Decision: 2021-04-23

General Information

Main field: Machine Learning, Systems and Control.
Compulsory for: MMSR1
Language of instruction: The course will be given in English

Aim

The course provides a review of concepts and methods needed for the master's programme in Machine Learning, Systems and Control. The focus lies on control systems fundamentals, statistics and linear system theory. The aim is that the students leave the course with a sufficient background for subsequent courses of the program. The course also aims to give the students a general introduction to studying in Lund, working in groups and presenting results orally and in writing. Furthermore, students are given an introduction to ethical aspects of AI and machine learning.

Learning outcomes

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

 

Contents

The course commences with a general and practical introduction to the study environment at LTH, and to working in groups with other students.

A large portion of the course revolves around a cyber-physical laboratory process. The students work in small groups to complete laboratory exercises aimed at demonstrating the link between theory and practice, and providing an opportunity to obtain knowledge through hands-on experience.

The technical content of the course, covered through exercises and laboratory work, reviews concepts and topics, which students are expected to have some familiarity with from previous studies. These include: models for stochastic dependence; concepts and models for description, characterising, and handling of stationary stochastic processes;  time and frequency domain description of stationary stochastic processes; covariance and effect spectrum; stochastic processes in linear filters; describing dynamical systems using time-invariant ordinary differential equations; transfer functions, frequency responses, Bode and Nyquist diagrams; stability assessment through poles and through the Nyquist criterion; robustness margins; synthesis and implementation of controllers.

Examination details

Grading scale: UG - (U,G) - (Fail, Pass)
Assessment: Written and peer-reviewed report; two blocks of laboratory exercises completed in groups of approximately 4 students; individually completed preparatory exercises (mathematics and programming) for each laboratory exercise block.

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.

Parts
Code: 0120. Name: Group Work.
Credits: 1,5. Grading scale: UG. Assessment: Inlämning, kamratgranskning och revision av rapport.
Code: 0220. Name: Laboratory Work.
Credits: 3. Grading scale: UG. Assessment: Completion of preparatory exercises and laboratory work.
Code: 0320. Name: Ability Test 1.
Credits: 1,5. Grading scale: UG. Assessment: Computer based test and computer exercise.
Code: 0420. Name: Ability Test 2.
Credits: 1,5. Grading scale: UG. Assessment: Computer based test and computer exercise.

Admission

Assumed prior knowledge: Courses equivalent to the admission criteria for the master programme in Machine Learning, Systems and Control.
The number of participants is limited to: No
The course overlaps following course/s: FRTF05, FRTN25, FRTF10

Reading list

Contact and other information

Course coordinator: Kristian Soltesz, kristian.soltesz@control.lth.se
Director of studies: Anton Cervin, anton.cervin@control.lth.se
Course homepage: http://www.control.lth.se/course/FRTF25
Further information: The course in open only to the students at the master´s program in Machine Learning, Systems and Control.