Course syllabus for Data analytics and machine learning in infrastructure and environmental engineering

Course syllabus adopted 2026-02-12 by Head of Programme (or corresponding).

Overview

  • Swedish nameDataanalys och maskininlärning inom infrastruktur och miljöteknik
  • CodeACE680
  • Credits7.5 Credits
  • OwnerMPIEE
  • Education cycleSecond-cycle
  • Main field of studyCivil and Environmental Engineering
  • DepartmentARCHITECTURE AND CIVIL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 27132
  • Minimum participants10
  • Open for exchange studentsYes

Credit distribution

0126 Project 7.5 c
Grading: TH		7.5 c

In programmes

Examiner

Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements

Course specific prerequisites

Bachelor degree in Civil, Transport, Environmental Engineering or equivalent.

Aim

This course aims to teach practical skills in big data analytics and machine learning for analyzing, modeling, and interpreting various datasets related to infrastructure, transportation, and environmental engineering.

Learning outcomes (after completion of the course the student should be able to)

  1. Understand different types of data and collection techniques relating to infrastructure, transport and environmental engineering.
  2. Identify methods for processing different types of data.   
  3. Understand the concepts and algorithms of different types of machine learning approaches.
  4. Illustrate the applicability of different machine learning approaches for solving tasks in infrastructure, transport and environmental engineering.
  5. Implement programming for data analytics relating to infrastructure, transport and environmental engineering.
  6. Apply and evaluate machine learning methods for practical tasks in infrastructure, transport and environmental engineering.

Content

The course includes concepts, knowledge, methods and practices about data analytics and machine learning for infrastructure, transport and environmental engineering. The following contents will be covered: (1) basic concepts and methods about data types, preprocessing and analysis; (2) concepts and algorithms of different types of machine learning; (3) programming knowledge and skills for data analytics and machine learning (4) Applications of data analytic and machine learning approaches for practical tasks in infrastructure, transport and environmental engineering. In the course, we will also explore potential career paths for data analytics in the fields of infrastructure, transport and environmental engineering.

Organisation

The course is structured to teach knowledge and skills in data analytics and machine learning for infrastructure, transport and environmental engineering. It begins with an overview of different data types, data collection methods, and data processing techniques. This is followed by concepts and methods for analyzing various types of data, together with the programming skills to implement these methods. The course then teaches the concepts and algorithms of widely used machine learning relevant to applications in infrastructure, transport, and environmental engineering, complemented by programming exercises. Thereafter, group-based project work is conducted to apply data analytics and machine learning methods to analyze and evaluate problems in infrastructure, transport and environmental engineering. Teaching is delivered through a combination of lectures, problem-solving exercises, and supervised group projects based on the students’ backgrounds and interests.

Literature

Coursera. Introduction to Data Analytics. Offered by IBM. Accessed January 30, 2026. https://www.coursera.org/learn/introduction-to-data-analytics. 
IBM. Machine Learning with Python. Coursera. Accessed January 30, 2026. https://www.coursera.org/learn/machine-learning-with-python
Wang, Yinhai, Zhiyong Cui, and Ruimin Ke. Machine learning for transportation research and applications. Elsevier, 2023.

Examination including compulsory elements

Course examination consists of exercises and project assignments.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.