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 2017) 2 1 English Linköping v
6MICS Computer Science, Master's Programme 1 (Autumn 2017) 2 1 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 7 (Autumn 2017) 2 1 English Linköping o
6CDDD Computer Science and Engineering, M Sc in Engineering 7 (Autumn 2017) 2 1 English Linköping v
6CITE Information Technology, M Sc in Engineering 7 (Autumn 2017) 2 1 English Linköping v
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 7 (Autumn 2017) 2 1 English Linköping o
6CMJU Computer Science and Software Engineering, M Sc in Engineering 7 (Autumn 2017) 2 1 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 7 (Autumn 2017) 2 1 English Linköping o

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Computer Science, Master's Programme
  • Computer Science and Engineering, M Sc in Engineering
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, 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

Additional 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

Additional literature

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.

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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