ITI41820 Advanced Topics in Machine Learning (Spring 2025)

The course goes in depth on selected topics and methods within machine learning and their applications. Examples include:

• advanced neural net and deep learning models, such as: ResNET, Zero shot, GAN, LSTM.
• Evolutionary and bio-inspired algorithms algorithms (like GA, EA, ES, PSO, ACO, AIS) in search, optimization and classification.
• Program induction. Symbolic regression. Automatic programming.
• Markov models, Kernel methods. SVM
• Implementing machine learning in Industries and business
• Machine learning challenges and future
• Philosophical fundamental problems and ethical questions related to machine learning

The course syllabus will continuously be updated with methods from state-of-the-art research. Other topics may be chosen by machine learning group members each year and may vary depending on who is involved.

Knowledge

The student

• knows the possibilities and advantages of employing the machine learning methods in the course as well as possible problems that may be encountered and how to overcome them
• knows how the algorithms presented in the course work and their characteristics, for example which problems they work best for, overfitting, expected accuracy and computational requirements, for example how much benefit that accelerators may provide

Skills

Given a machine learning application, the student is able to

• determine which theory and which methods that are presented in the course that are relevant and also how to apply them
• perform hyperparameter tuning or in some cases even perform modifications of the source codes
• use at least one implementation for each of the major machine learning techniques that are taught in the course

General competence

The student

• is able to independently read machine learning papers and other literature and evaluate what works well and what does not for new problems
• knows the terminology of machine learning and be familiar with the mathematics that is common in the field
• knows the general behaviour of machine learning methods for example regarding how much data that is required, how to preprocess the data and ensure that its quality is sufficient

The student must:

• give presentations at two seminars
• contribute with questions in at least two other seminars
• deliver a mid-term report

Coursework requirements must be accepted to qualify for the exam.

Individual project report and individual oral presentation

The assessment is based on an individual project report and an individual oral presentation.

The project report is given a tentative grade on the A - F grading scale. This grade can be adjusted at the oral presentation. The project report must be passed before the oral presentation can be carried out.

At the oral presentation the student presents and defends the project work. Approximately 30 minutes duration. No supporting materials are allowed except the presentation.

If the student decides to challenge the assessment, the project report must be re-assessed. If the new assessment affects the tentative grading of the report, a new oral presentation will be arranged

This course is evaluated by a

• Final course evaluation (compulsory)

The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee at the Department of Computer Science and Communication.