2024 Course dates: Introduction to practical machine learning

December 11, 2023

Our Senior Researcher, Mohammad Khalil, is teaching the next round of a PhD course on practical machine learning at the Faculty of Psychology. The course is arranged in collaboration with the International Graduate School in Integrated Neuroscience (IGSIN). Registration is open until the 21st of December.

Introduction to Practical Machine Learning is a new PhD course that has been developed by our Senior Researcher, Mohammad Khalil. During the course, Khalil will give students an intuitive introduction to machine learning, both in theory and practice. Machine learning is a branch of Artificial Intelligence (AI) and Computer Science, which focuses on how the use of data and algorithms might imitate the way that humans learn.

Khalil will be holding all the course lectures in English, and they will take place at the Faculty of Psychology during the Spring semester of 2024.

The PhD course has two main parts. The first part will cover the topic “Machine learning: What is it?”, describing how machine learning allows the discovery of knowledge from data. Since data is the fuel that drives machine learning, the course will dive into data management, where descriptions of the organization, storage, cleansing, filtration, and preparation of data collected and used in research projects will be explained.

Mohammad Khalil, Senior Researcher at SLATE. Photo: Eivind Senneset.

The second part of the course includes a group project where PhD candidates will work together on data sets. This will either be data sets of the candidates’ own choosing (in other words: PhD-related data sets) or open data sets. The candidates will apply machine learning techniques to distill interesting patterns and knowledge. They will have three weeks to work on the project and present their results in class.

R software will be used to manage practical examples of data management and machine learning. R is a programming language and software for statistical computing and graphics, supported by the R Core Team and the R Foundation for Statistical Computing. R is used by data miners, bioinformaticians and statisticians for data analysis and developing statistical software, and it is an open source, free software environment. During the course, R software will be approached from a beginner’s mindset, with students being walked through the creation and execution of their first R scripts.

Mohammad Khalil will cover multiple topics within machine learning, including data as a source for future decision-making and supervised and unsupervised learning techniques.

More About the Course

“Introduction to Practical Machine Learning” is one of the neuroscience and statistics courses presented by the International Graduate School in Integrated Neuroscience (IGSIN). IGSIN was founded as a joint initiative of the Faculty of Medicine and the Faculty of Psychology at the University of Bergen.

The primary goal of IGSIN is bringing together people from different countries with different educational backgrounds and different expertise, to provide an interdisciplinary training and networking platform for PhD students in the broad field of neuroscience. The field spans from basic biological, molecular, and genetic neuroscience to cognitive neuroscience, as well as clinical applications related to mental and neurological disorders.

The graduate school is located at the Department of Biological and Medical Psychology at the University of Bergen.

The registration deadline for the course is December 21st: Registration form

The course will have a maximum capacity of 20 students.

Course administrator: Vivian Helen Jacobsen Fosse.

Questions regarding course content: Contact Mohammad Khalil.

Course Dates

January 25: 09:30 – 14:30 (introduction to R)

February 1:  09:30 – 14:30

February 8:  09:30 – 14:30

February 15: 09:30 – 14:30

February 22: 09:30 – 14:30

March 12: 09:30 – 14:30, Group work presentation

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‍Visiting Address:

Christiesgate 12, 2nd floor

Postal Address:

University of Bergen
PO Box 7807
N-5020 Bergen, Norway