Machine Learning for Smart Cities, 6 credits

Maskininlärning för smarta städer, 6 hp

TNK129

Main field of study

Computer Science and Engineering Electrical Engineering

Course level

Second cycle

Course type

Programme course

Examiner

Marian Codreanu

Director of studies or equivalent

Erik Bergfeldt

Education components

Preliminary scheduled hours: 0 h
Recommended self-study hours: 160 h
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CKTS Communications, Transport and Infrastructure, Master of Science in Engineering 9 (Autumn 2026) 1 3 English Norrköping E
6CKTS Communications, Transport and Infrastructure, Master of Science in Engineering (Master Profile Smart Cities) 9 (Autumn 2026) 1 3 English Norrköping C
6MTSL Intelligent Transport Systems and Logistics, Master's Programme 3 (Autumn 2026) 1 3 English Norrköping E

Main field of study

Computer Science and Engineering, Electrical Engineering

Course level

Second cycle

Advancement level

A1N

Course offered for

  • Master of Science in Communications, Transport and Infrastructure
  • Master's Programme in Intelligent Transport Systems and Logistics

Prerequisites

Basic knowledge in linear algebra, calculus, statistics and probability theory as well as computer programming.  

Intended learning outcomes

In this course, you will learn how to utilize advanced models and deep learning architectures to learn from data in order to gain insights for decisions in smart city applications.

After completing the course, the student should be able to:

  • Explain assumptions and theory behind different type of machine learning methods
  • Explain and motivate differences in characteristics between different type of methods and give examples of when they should be applied
  • Evaluate and choose among different methods for a specific problem instance and dataset
  • Use existing datasets to train and evaluate different machine learning models 
  • Use selected machine learning models for prediction, inference and decision-making for smart city applications

Course content

The course aims to provide knowledge in machine learning. The course will cover both conventional machine learning methods as well as deep learning. The course content includes statistical inference, bias-variance tradeoff, Bayesian learning, Gaussian processes, support vector machines, kernels, neural networks, deep learning and reinforcement learning.

Teaching and working methods

Lectures, tutorials and labs.

Examination

UPG1Individual assignments2 creditsU, 3, 4, 5
PRA1Project work2 creditsU, G
LAB1Lab assignments2 creditsU, G

Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.

Grades

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

Other information

About teaching and examination language

The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows: 

  • If teaching language is “Swedish”, the course as a whole could be given in Swedish, or partly in English. Examination language is Swedish, but parts of the examination can be in English.
  • If teaching language is “English”, the course as a whole is taught in English. Examination language is English.
  • If teaching language is “Swedish/English”, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English depending on teaching language.

Other

The course is conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.

The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point. 

The course is campus-based at the location specified for the course, unless otherwise stated under “Teaching and working methods”. Please note, in a campus-based course occasional remote sessions could be included.  

Department

Institutionen för teknik och naturvetenskap

Course literature

Regulary literature

Books

  • Bishop, Christopher M., (2006) Pattern recognition and machine learning. New York, NY : Springer, cop. 2006
    ISBN: 0387310738, 9780387310732
  • Lindholm, Andreas, Verfasser, Wahlström, Niklas, Sonstige, Lindsten, Fredrik, Sonstige, Schön, Thomas B., Sonstige, (2022) Machine learning : a first course for engineers and scientists
    ISBN: 9781108843607, 1108843603

Websites

Additional literature

Books

  • Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, (2009) The elements of statistical learning : data mining, inference, and prediction. 2. ed New York : Springer, 2009
    ISBN: 9780387848570
  • James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert, (2021) An introduction to statistical learning : with applications in R. Second edition. New York : Springer, [2021]
    ISBN: 9781071614174, 1071614177, 1431875X, 1431875X
Code Name Scope Grading scale
UPG1 Individual assignments 2 credits U, 3, 4, 5
PRA1 Project work 2 credits U, G
LAB1 Lab assignments 2 credits U, G

Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.

Regulary literature

Books

Bishop, Christopher M., (2006) Pattern recognition and machine learning. New York, NY : Springer, cop. 2006

ISBN: 0387310738, 9780387310732

Lindholm, Andreas, Verfasser, Wahlström, Niklas, Sonstige, Lindsten, Fredrik, Sonstige, Schön, Thomas B., Sonstige, (2022) Machine learning : a first course for engineers and scientists

ISBN: 9781108843607, 1108843603

Websites

Lecture notes from Stanford course CS229 http://cs229.stanford.edu/syllabus-spring2021.html

Additional literature

Books

Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, (2009) The elements of statistical learning : data mining, inference, and prediction. 2. ed New York : Springer, 2009

ISBN: 9780387848570

James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert, (2021) An introduction to statistical learning : with applications in R. Second edition. New York : Springer, [2021]

ISBN: 9781071614174, 1071614177, 1431875X, 1431875X

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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