Skriv ut Lukk vindu


 

Vår 2025

FYS-8603 Winter School NLDL 2025 - 5 stp


The course is administrated by

Institutt for fysikk og teknologi

Type of course

The course can be taken as a singular course. Registration is open for UiT students, members of the NORA Research School, and NLDL participants. It will be conducted as a concentrated course in the style of winter school 6th to 10th Jan 2025.

Course contents

This course will provide a study of several emerging topics of high relevance within advanced deep learning, from a basic understanding of the techniques to the latest state-of-the-art developments in the field.

The course will consist of 5 full days of the NLDL conference including: tutorials, keynote sessions, and practical components.

Core concepts:

Relevance of course in program of study: Deep learning has revolutionized technology and science by enabling information to be extracted with much better precision and at much larger scales compared to only a few years ago from data sources such as images, text, speech, biological material, chemical components, and sensory measurements in general. Most study programs offer courses on the fundamental theory and applications of neural networks and machine learning systems in general, which provide the backbone of deep learning. This course treats topics within deep learning that are not covered in standard courses. The course consists of topics such as in-depth exploration of uncertainty in machine learning (ML), covering its significance, challenges, and representation in statistics. It distinguishes between aleatoric and epistemic uncertainty, emphasizing their implications in practical ML tasks. The course introduces novel approaches for uncertainty quantification and examines case studies demonstrating its importance in real-world applications. In addition, this course also explores Responsible AI (RAI), discussing ethical, legal, and societal impacts, and methods to embed ethical principles into AI systems. Within RAI, socio-technical approaches to the governance, monitoring and control of intelligent systems are discussed with the help of a simulated responsible design problem. In addition, the course’s curriculum includes large language models (LLMs), focusing on their role in NLP, pre-training, and fine-tuning, with hands-on sessions using open-source LLMs for practical applications. In this session, state-of-the-art research problems associated with generative LLMs are discussed.


Admission requirements

PhD students or holders of a Norwegian master’s degree of five years (300 ECTS) or 3 (180 ECTS) + 2 years (120 ECTS) or equivalent may be admitted. PhD students do not have to prove English proficiency and are exempt from a semester fee. Holders of a master’s degree must upload a master´s diploma with diploma supplement in English. PhD students at UiT The Arctic University of Norway can register for the course through StudentWeb. External applicants apply for admission through SøknadsWeb. All external applicants must attach a confirmation of their status as a PhD student from their home institution. Please note that students who hold a master of science degree but are not yet enrolled as a PhD student must attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

Recommended prerequisites: Programming skills in Python and hands-on knowledge of Python programming for deep learning. Knowledge of machine learning at master’s level from study programs in computer science, physics and technology, mathematics and statistics, or equivalent.

Application code: 9316, application deadline: 15 November 2024.

The course is limited to 40 participants. Qualified applicants are ranked on the basis of a lottery if there are more applicants than available places.


Objective of the course

Knowledge - The student is able to

Skills - The student is able to

General competence - The student is able to

work towards understating LLMs and pretraining and fine tuning LLMs, interacting, working, and evaluation of LLMs for a practical task.


Language of instruction

The language of instruction is English, and all the syllabus material is in English. The final report needs to be submitted in English.

Teaching methods

Lectures: 40 hours (full 5 days of the winter school)

Self-study sessions: 40 hours

Project work: spread over 8 weeks - net time 50 hours

Hands-on sessions: 10 hours

Net effort (~120 hours)