Machine Learning, 6 credits (TDDE01)

Maskininlärning, 6 hp

Main field of study

Information Technology Computer Science and Engineering Computer Science

Level

Second cycle

Course type

Programme course

Examiner

Oleg Sysoev

Director of studies or equivalent

Ann-Charlotte Hallberg
Course offered for Semester Period Timetable module Language Campus VOF
6MDAV Computer Science, Master's Programme 1 (Autumn 2019) 2 1 English Linköping v
6MICS Computer Science, Master's Programme 1 (Autumn 2019) 2 1 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 7 (Autumn 2019) 2 1 English Linköping o
6CDDD Computer Science and Engineering, M Sc in Engineering 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Spanish 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Spanish (Specialization Computer Science and Engineering) 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - French 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - French (Specialization Computer Science and Engineering) 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Japanese 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Japanese (Specialization Computer Science and Engineering) 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - German 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - German (Specialization Computer Science and Engineering) 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Chinese 7 (Autumn 2019) 2 1 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering - Chinese (Specialization Computer Science and Engineering) 7 (Autumn 2019) 2 1 English Linköping v
6CIII Industrial Engineering and Management, M Sc in Engineering 7 (Autumn 2019) 2 1 English Linköping v
6CIII Industrial Engineering and Management, M Sc in Engineering (Computer Science and Engineering Specialization) 7 (Autumn 2019) 2 1 English Linköping v
6CITE Information Technology, M Sc in Engineering 7 (Autumn 2019) 2 1 English Linköping v
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 7 (Autumn 2019) 2 1 English Linköping o
6CMJU Computer Science and Software Engineering, M Sc in Engineering 7 (Autumn 2019) 2 1 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 7 (Autumn 2019) 2 1 English Linköping o
6CDDD Computer Science and Engineering, M Sc in Engineering (Medical Informatics) 9 (Autumn 2019) 2 1 English Linköping v
6CITE Information Technology, M Sc in Engineering (Medical Informatics) 9 (Autumn 2019) 2 1 English Linköping v
6MICS Computer Science, Master's Programme (AI and Data Mining) 1 (Autumn 2019) 2 1 English Linköping v
6CITE Information Technology, M Sc in Engineering (Communication) 7 (Autumn 2019) 2 1 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CITE Information Technology, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CDPU Design and Product Development, M Sc in Engineering 7 (Autumn 2019) 2 1 English Linköping v
6CYYY Applied Physics and Electrical Engineering, M Sc in Engineering (Applied Physics - Theory, Modelling and Computation) 7 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Chinese (Applied physics -Theory, Modelling and Computation) 7 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Japanese (Applied physics -Theory, Modelling and Computation) 7 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, Spanish (Applied physics -Theory, Modelling and Computation) 7 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, German (Applied physics -Theory, Modelling and Computation) 7 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering, French (Applied physics -Theory, Modelling and Computation) 7 (Autumn 2019) 2 1 English Linköping v
6CYYY Applied Physics and Electrical Engineering, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 9 (Autumn 2019) 2 1 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (Communication) 7 (Autumn 2019) 2 1 English Linköping v

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Master's Programme in Computer Science
  • Computer Science and Engineering, M Sc in Engineering
  • Industrial Engineering and Management - International, M Sc in Engineering
  • Industrial Engineering and Management, M Sc in Engineering
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering
  • Design and Product Development, M Sc in Engineering
  • Applied Physics and Electrical Engineering, M Sc in Engineering
  • Applied Physics and Electrical Engineering - International, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

Probability theory; Statistics; Mathematical analysis; Linear Algebra; Basic programming.

Intended learning outcomes

The overall aim of the course is to provide an introduction to machine learning, with special focus on regression and classification problems. Machine learning is presented from a probabilistic perspective with inference and prediction based on probability models. The course aims to give students an overview of machine learning within a unified framework and a good basis for further studies in the field.

After completing the course the student should be able to:

  • use relevant concepts and methods in machine learning to formulate, structure and solve practical problems.

  • infer the parameters in a number of common machine learning models.

  • use machine learning models for prediction and decision making.

  • evaluate and choose among models.

  • implement machine learning models and algorithms in a programming language.

Course content

Introduction and overview of machine learning and its applications. Unsupervised and supervised learning. Discriminative and generative models. Prediction. Generalization. Classification. Nearest neighbors. Naïve Bayes. Discriminant analysis. Cross-validation. Model selection. Overfitting. Bootstrap. Regression. Regularization. Ridge regression. Lasso. Variable Selection. Binary and multi-class regression. Dimension reduction. PCA. ICA. Kernel smoothers. Support Vector Machines. Decision trees. Gaussian processes. Mixture models.

Teaching and working methods

The course consists of lectures and computer laboratory work. The lectures introduce concepts and theories that students then use in problem solving at the computer labs.

Examination

DAT1Computer examinationU, 3, 4, 53 credits
LAB1Laboratory workU, G3 credits

DAT1 is an exam in a computer hall that tests students' theoretical knowledge and problem-solving skills in machine learning. UPG1 consists of computer exercises that tests the students' ability to translate theoretical knowledge into practical problem solving in machine learning.

Grades

Four-grade scale, LiU, U, 3, 4, 5

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Ann-Charlotte Hallberg

Examiner

Oleg Sysoev

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Course literature

Books
Bishop, C. M, (2006) Pattern Recognition and Machine Learning SpringerHastie, T., Tibshirani, R., och Friedman J., (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction 2:a upplagan Springer

Books

Bishop, C. M, (2006) Pattern Recognition and Machine Learning Springer
Hastie, T., Tibshirani, R., och Friedman J., (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction 2:a upplagan Springer
DAT1 Computer examination U, 3, 4, 5 3 credits
LAB1 Laboratory work U, G 3 credits

DAT1 is an exam in a computer hall that tests students' theoretical knowledge and problem-solving skills in machine learning. UPG1 consists of computer exercises that tests the students' ability to translate theoretical knowledge into practical problem solving in machine learning.

Course syllabus

A syllabus has been established for each course. The syllabus specifies the aim and contents of the course, and the prior knowledge that a student must have in order to be able to benefit from the course.

Timetabling

Courses are timetabled after a decision has been made for this course concerning its assignment to a timetable module. A central timetable is not drawn up for courses with fewer than five participants. Most project courses do not have a central timetable.

Interrupting a course

The vice-chancellor’s decision concerning regulations for registration, deregistration and reporting results (Dnr LiU-2015-01241) states that interruptions in study are to be recorded in Ladok. Thus, all students who do not participate in a course for which they have registered must record the interruption, such that the registration on the course can be removed. Deregistration from a course is carried out using a web-based form: www.lith.liu.se/for-studenter/kurskomplettering?l=sv. 

Cancelled courses

Courses with few participants (fewer than 10) may be cancelled or organised in a manner that differs from that stated in the course syllabus. The board of studies is to deliberate and decide whether a course is to be cancelled or changed from the course syllabus. 

Regulations relating to examinations and examiners 

Details are given in a decision in the university’s rule book: http://styrdokument.liu.se/Regelsamling/VisaBeslut/622678.

Forms of examination

Examination

Written and oral examinations are held at least three times a year: once immediately after the end of the course, once in August, and once (usually) in one of the re-examination periods. Examinations held at other times are to follow a decision of the board of studies.

Principles for examination scheduling for courses that follow the study periods:

  • courses given in VT1 are examined for the first time in March, with re-examination in June and August
  • courses given in VT2 are examined for the first time in May, with re-examination in August and October
  • courses given in HT1 are examined for the first time in October, with re-examination in January and August
  • courses given in HT2 are examined for the first time in January, with re-examination at Easter and in August.

The examination schedule is based on the structure of timetable modules, but there may be deviations from this, mainly in the case of courses that are studied and examined for several programmes and in lower grades (i.e. 1 and 2). 

  • Examinations for courses that the board of studies has decided are to be held in alternate years are held only three times during the year in which the course is given.
  • Examinations for courses that are cancelled or rescheduled such that they are not given in one or several years are held three times during the year that immediately follows the course, with examination scheduling that corresponds to the scheduling that was in force before the course was cancelled or rescheduled.
  • If teaching is no longer given for a course, three examination occurrences are held during the immediately subsequent year, while examinations are at the same time held for any replacement course that is given, or alternatively in association with other re-examination opportunities. Furthermore, an examination is held on one further occasion during the next subsequent year, unless the board of studies determines otherwise.
  • If a course is given during several periods of the year (for programmes, or on different occasions for different programmes) the board or boards of studies determine together the scheduling and frequency of re-examination occasions.

Registration for examination

In order to take an examination, a student must register in advance at the Student Portal during the registration period, which opens 30 days before the date of the examination and closes 10 days before it. Candidates are informed of the location of the examination by email, four days in advance. Students who have not registered for an examination run the risk of being refused admittance to the examination, if space is not available.

Symbols used in the examination registration system:

  ** denotes that the examination is being given for the penultimate time.

  * denotes that the examination is being given for the last time.

Code of conduct for students during examinations

Details are given in a decision in the university’s rule book: http://styrdokument.liu.se/Regelsamling/VisaBeslut/622682.

Retakes for higher grade

Students at the Institute of Technology at LiU have the right to retake written examinations and computer-based examinations in an attempt to achieve a higher grade. This is valid for all examination components with code “TEN” and "DAT". The same right may not be exercised for other examination components, unless otherwise specified in the course syllabus.

Retakes of other forms of examination

Regulations concerning retakes of other forms of examination than written examinations and computer-based examinations are given in the LiU regulations for examinations and examiners, http://styrdokument.liu.se/Regelsamling/VisaBeslut/622678.

Plagiarism

For examinations that involve the writing of reports, in cases in which it can be assumed that the student has had access to other sources (such as during project work, writing essays, etc.), the material submitted must be prepared in accordance with principles for acceptable practice when referring to sources (references or quotations for which the source is specified) when the text, images, ideas, data, etc. of other people are used. It is also to be made clear whether the author has reused his or her own text, images, ideas, data, etc. from previous examinations.

A failure to specify such sources may be regarded as attempted deception during examination.

Attempts to cheat

In the event of a suspected attempt by a student to cheat during an examination, or when study performance is to be assessed as specified in Chapter 10 of the Higher Education Ordinance, the examiner is to report this to the disciplinary board of the university. Possible consequences for the student are suspension from study and a formal warning. More information is available at https://www.student.liu.se/studenttjanster/lagar-regler-rattigheter?l=sv.

Grades

The grades that are preferably to be used are Fail (U), Pass (3), Pass not without distinction (4) and Pass with distinction (5). Courses under the auspices of the faculty board of the Faculty of Science and Engineering (Institute of Technology) are to be given special attention in this regard.

  1. Grades U, 3, 4, 5 are to be awarded for courses that have written examinations.
  2. Grades Fail (U) and Pass (G) may be awarded for courses with a large degree of practical components such as laboratory work, project work and group work.

Examination components

  1. Grades U, 3, 4, 5 are to be awarded for written examinations (TEN).
  2. Grades Fail (U) and Pass (G) are to be used for undergraduate projects and other independent work.
  3. Examination components for which the grades Fail (U) and Pass (G) may be awarded are laboratory work (LAB), project work (PRA), preparatory written examination (KTR), oral examination (MUN), computer-based examination (DAT), home assignment (HEM), and assignment (UPG).
  4. Students receive grades either Fail (U) or Pass (G) for other examination components in which the examination criteria are satisfied principally through active attendance such as other examination (ANN), tutorial group (BAS) or examination item (MOM).

The examination results for a student are reported at the relevant department.

Regulations (apply to LiU in its entirety)

The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.

LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva. 

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