Through the EpiMed Open Course initiative, the Institut pour l’Avancée des Biosciences (IAB) and Grenoble Alpes University have launched courses available on Youtube. Researcher Ekaterina Flin offers here an introduction to artificial intelligence for the analysis of biological data and the study of cancer.
The course provides an overview of application areas and scientific questions that can be solved by AI approaches. It presents the basic principles of some classical methods in machine learning: logistic regression, SVM, neural networks. The course is illustrated with examples of the use of omics data for cancer diagnosis and prognosis.
Program :
- Artificial intelligence, machine learning, deep learning
- Machine learning versus expert systems
- Large classes of learning problems: supervised, unsupervised, by reinforcement
- Presentation of some classic methods
- Common Difficulties with AI Approaches and Possible Solutions
- Examples of the use of AI methods for the diagnosis and prognosis of cancers using omics data
Timeline:
00:00 Course outline
01:26 Definition and history of AI
09:28 Examples of AI applications
18:32 Problem classes in machine learning
21:02 First example of prediction
29:22 Conventional machine learning methods for omics data
49:57 Examples in research projects at EpiMed
Links mentioned in the course :
– JDEV2020 Programming and deploying your AI: http: //devlog.cnrs.fr/jdev2020/t8
– MIT Introduction to Deep Learning 6.S191: https: //bit.ly/2JeFcW0
– TED How to take a picture of a black hole : https://bit.ly/39doPUp
– ProPublica analysis of COMPAS AI algorithm: https: //bit.ly/37aGSb4
Translated from Intelligence artificielle pour les biologistes, une introduction d’EpiMed Open Course initiative
