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  1. ML and AI in the afternoon
  2. Practical intro to computer clusters
  3. Introduction to Vera
  4. Introduction to Alvis
  5. Introduction to Research Data Management (RDM)
  6. Computer Vision
  7. Visualization

ML and AI in the afternoon

Target group: PhD students + Researchers at Chalmers

Level: Introductory course, Basic level

Time. 13:00- 16:00 (3 hours)

The course aims to provide the participants (researchers) with an overview of the general concepts and fundamentals of Artificial Intelligence AI and Machine Learning ML. It offers an understanding of the ML terminologies and techniques: supervised learning ( Regression and Classification) and unsupervised learning (Clustering and Association).

The course shows the participants how to apply various methods for ML to solve different types of problems and choose between multiple ML algorithms that fit the application; then, the researchers can build their ML applications and learn the practical aspect of evaluation, such as validation techniques and understanding the metrics.

Furthermore, the purpose is also to understand the different forms of data (images, text, numerical and others) in addition to data preparation. The course is also customised according to the targeted department each time, so the participants can figure out where ML could be applicable in their field. 

In addition, the course gives the participants a quick overview of Deep Learning and introduces them to the most well-known networks.
Finally, there will be a coding session where we apply the concepts covered during the course to a real example from the participant’s knowledge.

Registration is open for the Department of Architecture and Civil Engineering
(not later than 15th Oct, 2023): here

Practical Introduction to computer clusters

– Target group: Students in courses that will be using Vera or Alvis

– Level: Basic (those who can’t follow along in the Vera and Alvis intros would probably benefit from starting with this one before trying again)

This online self-study course introduces concepts that are good to know when working with most computer clusters. Including but not limited to working with the command line in Linux, the SLURM job scheduler and using containers. While the material is specifically tailored for the Vera and Alvis computer clusters, the content is valuable when working with larger computer clusters.

Introduction to Vera

 – Target group: New users of Vera

 – Level: Familiarity with computers is expected

 A 2-hour seminar introducing the Vera computer cluster and how to work with it. All new users should are expected to participate, and those that are unable to attend should read through the slides.

Introduction to Alvis

 – Target group: New users of Alvis

 – Level: Familiarity with computers is expected

A 2-hour seminar introducing the Alvis computer cluster and how to work with it. In connection, a 2-hour workshop is usually held with exercises for getting started with machine learning on Alvis. All new users should are expected to participate in the seminar, and those that are unable to attend should read through the slides.

Introduction to Research Data Management (RDM)

Target Group: PhD students + Researchers [can be adapted]
Course Level: Beginner

The course’s name and contents can be adapted depending on the target audience. We have a base/standard material where we go through the basics of RDM, which are those listed in the previous agenda I sent you. Those points will always be mentioned during such a course, but the focus will vary depending on the audience. 

Computer Vision

This course introduces methods for analyzing and understanding images through a Machine Learning perspective. Computer Vision aims to infer something about the world using observed image data with applications such as Image Classification, Localization, Object Detection and Segmentation.

The course shows examples of dealing with such problems using methods in the field of Deep Neural Networks and the concept of feature learning. We also look at recent advancements in generative models and self-supervised learning.


Visualization is an essential part of the data science pipeline, either for presenting and delivering a message to a specific audience or as a tool for exploration to get more understanding of a dataset. The main goal is to help our understanding of the data by utilizing our human ability to find patterns visually.

This course shows various ways of working with the visualization of data through different kinds of tools and visual elements. We further discuss the importance of visual interaction, how to deal with high-dimensional data and show examples of working with geospatial and temporal data.