Dr. Daouda supervises PhD students, postdoctoral fellows, MSc students, and undergraduate researchers, with an emphasis on rigorous experimental design, reproducibility, communication, and collaboration.
Dr. Daouda teaches Data Science and AI to Medicine and Pharmacy students at the Faculty of Medical Sciences of the University Mohammed VI Polytechnic (UM6P)
The course follows a six semesters accelerated program starting with the foundational mathematical concepts and the basics of Data Science and AI, finishing with the more advanced concepts
of modern biomedical applications and research.
The goal is to build strong multidisciplinary skills in all aspects of modern AI. At the end students should be able to design, write and train their own models for biomedical applications and research, and justify their choices based on theoretical and empirical evidence.
This one hour video recapilates most of the important concepts of the first two years
Module description: This module teaches the basics of programming, data science and machine learning for medicine and biology. Students will learn how machine learning models can be integrated into research and clinical settings. The aim is practical rather than theoretical with the emphasis being on learning to use readily available tools to analyze and derive meaningful insights from datasets. This course will also prepare students for the more advanced second phase where the emphasis is on more complex deep learning methods.
Learning outcomes : On successful completion students should have a good understanding of how machine learning models are evaluated and should be able to integrate a lab and autonomously perform analyses using ready made tools.
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Module description: This module is an introduction the basic concepts of modern AI and deep neural networks.
Learning outcomes : On successful completion students should have a good understanding of how AI models are implemented and should be able to implement and train basic AIs.
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Module description: This module teaches more advanced techniques of AI and Deep Learning.
Learning outcomes : On successful completion students should have a good understanding of modern AI and be able to use relatively complex models.
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Module description: This module focuses on more advanced techniques for generative models and self-supervision.
Learning outcomes : At the end of the semester students should be proficient in AI capable of including advanced techniques in their use of AI.
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Module description: This semester focuses on drug discovery and the more advanced techniques of AI used in the industry.
Learning outcomes : On successful completion students should have a very grasp of AI for biomedical applications, and should be able to have a basic understanding of AI papers as well as a critical view on applied AI papers.
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Module description: This final semester students are 100% focused on an building and AI project for a biomedical application.
Learning outcomes : Students should demonstrate their mastery of AI through an ambitious project applying AI to a biomedical application and preparing them to join a research laboratory.
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