INF-8606 Generative AI for health and life sciences - 5 ECTS

In today's rapidly advancing technological landscape, the fusion of artificial intelligence and life sciences is creating groundbreaking opportunities for innovation and discovery. This course is designed to equip students with fundamental knowledge essential for navigating this dynamic intersection, particularly focusing on the realm of Generative AI and its applications within life science.

Registration is closed.

 

Note, the first lecture will be in the end of May and will be given digitally.

 

Throughout this course, we will delve into the practical applications of generative AI, exploring how it can be utilized to visualize, synthesize, and comprehend complex biological phenomena. By doing so, students will gain valuable insights into how these techniques can enhance their studies and contribute to advancements within their respective fields. Moreover, this course serves as a nexus between the exponential growth of artificial intelligence and the ever-evolving landscape of life sciences. 

In essence, this course provides a friendly introduction to the preliminary concepts required to understand and employ generative AI to the life science community. Further, it explores the applications of generative AI in the context of life sciences. The aim of this initiative is to bridge the knowledge gap for those keen on delving deeper into the intersections of generative AI and life sciences. Recognizing the importance of generative AI techniques for life science research, and the future prospects of their collaborations, is central to this course's ethos.

 

Introductory concepts:

  • Introduction to Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI
  • Different applications of generative AI
  • Impact of generative AI on life science research

Prerequisites topics:

  • Convolutional Neural Networks
  • Natural Language Processing
  • Large Language Models
  • Generative AI

Application development requirements:

  • Programing language
  • Frameworks
  • Attention
  • Transformer
  • Synthetic image/video generation

Generative AI applications:

  • Synthetic data generation for life sciences
  • Computational biology: visualization and prediction
  • Language modeling for life sciences

Self-reading

  • Two research articles - comparison, weaknesses, strengths, application domain
  • Extensive lab work, self-exercises and groups are planned for competence development is also included.

 

The course is supported by the Digital Life Norway Research School and there are a limited number of travel grants available for our members. 

 

Contact course coordinator:

Dilip K. Prasad dilip.prasad@uit.no

Published Mar. 8, 2024 11:55 AM - Last modified May 30, 2024 3:04 PM