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This course equips engineers with the foundational knowledge and practical skills to develop, evaluate, and optimize chatbot and dialogue systems using large language models (LLMs). Through hands-on exercises, participants will learn to assess model performance, integrate information retrieval, and enhance chatbot capabilities using prompt engineering, fine-tuning, and web data.

Content & dates: This course is divided into 4 days with 2 modules in each:

  • Day 1: LLM & Evaluation Foundations.
  • Day 2: Prompt Engineering and Augmenting with Retrieved Information (RAG)
  • Day 3: Information Retrieval and Real-time Data for Agents
  • Day 4: LLM Fine-Tuning

Prerequisites: To will need the following knowledge to successfully be able to complete the course, either through formal education or self-study. If you are uncertain about complying with the prerequisites, please contact us for an evaluation.

  • Knowledge of machine learning - probability theory, linear algebra, classification, neural networks.
  • Programming experience in Python

Workload: The total number of hours calculated to be 44 hours distributed as follows:

  • Teaching & exercises: 24 hours
  • Preparation: 20 hours

Teachers: This course is taught by Associate Professors Daniel Hershcovich & Pepa Kostadinova Atanasova

Price: 30.000 kr.

Note: If there are less than 8 participants, we reserve the right to cancel the course.

Sign up here

 

 

In recent years, Python has evolved into the de-facto language of data-science and machine-learning. This toolbox course introduces python and its many data-science centric libraries. The goal is to provide the participants with useful tools to read and preprocess data and to apply the full machine-learning pipelines to it. 
As the course progresses, we will look at more complex libraries, introducing auto-differentiation and optimization of models. This is a hands-on course, and participants should expect to solve many programming related exercises.
During the course, you will work on a project where it will be possible to work on your own datasets, giving you an opportunity to dive into real life challenges related to the topic in your workplace. The course also comes with its own examples and datasets from different areas in science to support the teaching.

Content & dates: The course is divided into 3 modules consisting of two days:

  • Module 1: The Basics of Data Science in Python (20th & 21st April 2026)
  • Module 2: Introduction to Machine Learning (11th & 12th May 2026)
  • Project: Solving machine learning problems with own datasets including 1hour project supervision with teacher (between Module 2 & 3)
  • Module 3: Deep Learning with Pytorch (1st & 2nd June 2026)

Prerequisites: If you are uncertain about complying with the prerequisites, please contact us for an evaluation.

  • Basic programming in Python
  • Linear algebra (1st year university level)
  • Basic concepts of statistics (1st year university level)

Workload: The total number of hours calculated to be 70-80 hours distributed as follows:

  • Teaching & exercises: 36 hours
  • Preparation: 18 hours
  • Project hours: 10-20 hours

Teacher: This course is taught by Associate Professor Oswin Krause

Price: 30.000 kr.

Note: If there are less than 10 participants, we reserve the right to cancel the course.

Sign up here