Course syllabus

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Course-PM

ACE680 Data Analytics and Machine Learning in Infrastructure and Environmental Engineering LP1 (7.5 hp)

Course Code

ACE 680

Course

Data Analytics and Machine Learning in Infrastructure and Environmental Engineering

Coordinating Unit

Department of Architecture and Civil Engineering

Term

Period 1, Autumn 2026

Level

Master

Location/s

The course rooms are SB-L308 or L408

Details at Chalmers - ACE680_50_HT26_27132, Dataanalys och mas...

Duration and timetable

8 weeks and 7.5-hours in class activities per week

The full timetable of all activities for this course can be accessed from the “syllabus” module of this course in Canvas 

 

Course Description

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.

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.

The course does not require previous experience in Python coding. The course will teach from beginning about using Python.

 

Course Objectives

After the completion of this course, the students 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.

 

Schedule

Time edit: Chalmers - ACE680_50_HT26_27132, Dataanalys och mas...

Course schedules and details: Course schedule for ACE680_final_updated.pdf

 

Course literature

All required materials will be available in Canvas.

Copies of PowerPoint slides, recordings of lectures and related references for each part, will be made available to students in Canvas as well.

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.
LLM such as OpenAI or Claude could be used for learning but cannot replace manual coding practice or work in quiz and projects

 

Course tasks and assessment

 The course will involve the following teaching and learning components: lectures; in-class exercise; tutorials; homework; and projects. The schedule for the above contents will be available on Canvas. The assessment of the course is based on performances in these components. Quiz assignments are individual tasks. Tutorials are given to enhance and recall the learned knowledge on time. Project work be a group work with 2-3 people.

The assessment of this course is based on the following four principles:

  • Encourage active learning and reinforce learning.
  • Robustly and fair evaluate student performance in the course.
  • Fair and equitable for students to demonstrate what they have learned and their efforts.
  • Maintain academic standards.

The detailed assessment criterion is summarized below.

Assessment Tasks

Weighting (%)

Individual/ Group

Due (week)

Two in-class quiz test

8/8

Individual

Available in the schedule file at Canvas

Project 1/2/3

28/28/28

Group

 

Course Grading System for the Exam and the Whole Course

The perfect score is 100. Different grades are in the below table. The thresholds for different final grade level may be adjusted based on the situations if necessary.

Final overall score

Grade in Ladok

0-60

 Fail

60-75

3

75-90

4

90 or above

5

 

Teachers

Kun Gao, gkun@chalmers.se 

Kathleen Murphy, murphyk@chalmers.se

Ezra Haaf, ezra.haaf@chalmers.se 

Ali Esmaeeli, esmaeeli@chalmers.se 

Omkar Parishwad, omkarp@chalmers.se

 

Student representatives

Klara Backman, klarisu@icloud.com 

Alice Lundgren, lundgren.alice@hotmail.com

Wenqi Qiao, wenqiq@chalmers.se

Ali Saad, 123ali.saad03@gmail.com

Ella Sofia Paulina Zetterberg, ella.zetterberg@outlook.com 

 

Course summary:

Course Summary
Date Details Due