Advanced Machine Learning, 6 credits (TDDE15)

Avancerad maskininlärning, 6 hp

Main field of study

Computer Science and Engineering Computer Science

Level

Second cycle

Course type

Programme course

Examiner

Jose M Pena

Director of studies or equivalent

Ann-Charlotte Hallberg
Course offered for Semester Period Timetable module Language Campus VOF
6CDDD Computer Science and Engineering, M Sc in Engineering 9 (Autumn 2018) 1 1 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2018) 1 1 English Linköping v
6CITE Information Technology, M Sc in Engineering 9 (Autumn 2018) 1 1 English Linköping v
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2018) 1 1 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering 9 (Autumn 2018) 1 1 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2018) 1 1 English Linköping v

Main field of study

Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • 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 and Statistics; Bayesian Learning; Machine Learning; Mathematical analysis; Linear Algebra; Basic programming.

Intended learning outcomes

The course presents the analysis of several large classes of models widely used in advanced machine learning, such as state-space models, gaussian processes, hidden Markov models, Bayesian networks, and Markov random fields. Students will learn about the structure and learning of these models, when they are applicable, how to use them in practical machine learning applications, and how to correctly interpret the results. The models are mainly analyzed from a Bayesian perspective.
After completing the course, the student should be able to:

  • use the introduced model classes to accurately formulate and solve practical problems.
  • learn the parameters and perform predictions in the presented models.
  • evaluate and choose among the models within each class.
  • implement the models and learning methods in a programming language. 

Course content

Bayesian learning summary, Gaussian processes, State-space models, Kalman filtering and smoothing, Particle methods, Graphical models, Bayesian networks, Markov models, Hidden Markov models, Markov random fields.

Teaching and working methods

The course consists of lectures, seminars and computer laboratory work. The lectures introduce concepts and theories that students then use in problem solving at the computer labs. Seminars comprise student presentations and discussion of computer lab reports.

Examination

DAT1Computer examinationU, 3, 4, 53 credits
UPG1Computer-based laboratory exercisesU, 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

Other information



Supplementary courses:
Text Mining, Visual Object Recognition and Detection

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Ann-Charlotte Hallberg

Examiner

Jose M Pena

Education components

Preliminary scheduled hours: 52 h
Recommended self-study hours: 108 h

Course literature

Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006.
Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006.
DAT1 Computer examination U, 3, 4, 5 3 credits
UPG1 Computer-based laboratory exercises 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.
 

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