Machine Learning for Smart Cities, 6 credits

Main field of study

Course level

Advancement level

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

Grades

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

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

• 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