Advanced Machine Learning, 6 credits

Avancerad maskininlärning, 6 hp

TDDE15

Main field of study

Computer Science and Engineering Computer Science

Course level

Second cycle

Course type

Programme course

Examiner

Jose M Pena

Director of studies or equivalent

Ann-Charlotte Hallberg

Education components

Preliminary scheduled hours: 52 h
Recommended self-study hours: 108 h
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CDDD Computer Science and Engineering, M Sc in Engineering 9 (Autumn 2017) 1 1 English Linköping, Valla E
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2017) 1 1 English Linköping, Valla E
6CMJU Computer Science and Software Engineering, M Sc in Engineering 9 (Autumn 2017) 1 1 English Linköping, Valla E
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2017) 1 1 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering 9 (Autumn 2017) 1 1 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 9 (Autumn 2017) 1 1 English Linköping, Valla E

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

UPG1Computer-based laboratory exercises3 creditsU, G
DAT1Computer examination3 creditsU, 3, 4, 5


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.
Code Name Scope Grading scale
UPG1 Computer-based laboratory exercises 3 credits U, G
DAT1 Computer examination 3 credits U, 3, 4, 5


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.
 

Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006.

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize
I U A Modules Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
1.1 Knowledge of underlying mathematics and science (G1X level)

                            
1.2 Fundamental engineering knowledge (G1X level)

                            
1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)

                            
1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)

                            
1.5 Insight into current research and development work

                            
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
2.1 Analytical reasoning and problem solving

                            
2.2 Experimentation, investigation, and knowledge discovery

                            
2.3 System thinking

                            
2.4 Attitudes, thought, and learning

                            
2.5 Ethics, equity, and other responsibilities

                            
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
3.1 Teamwork

                            
3.2 Communications

                            
3.3 Communication in foreign languages

                            
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
4.1 External, societal, and environmental context

                            
4.2 Enterprise and business context

                            
4.3 Conceiving, system engineering and management

                            
4.4 Designing

                            
4.5 Implementing

                            
4.6 Operating

                            
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development

                            
5.2 Economic conditions for knowledge development

                            
5.3 Identification of needs, structuring and planning of research or development projects

                            
5.4 Execution of research or development projects

                            
5.5 Presentation and evaluation of research or development projects

                            

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