Through a hands-on approach and real-world datasets, this course will provide students with a comprehensive understanding of machine learning and its applications in the field of aging and longevity. Topics covered include the data generating process, model selection and evaluation, generalized linear models, various supervised and unsupervised machine learning algorithms (such as support vector machines, decision trees, random forests, neural networks, and k-means), and ethical considerations in artificial intelligence and machine learning.
Students will learn how to implement machine learning methods effectively, including the assessment of assumptions about the data generating process, the creation of relevant features, the handling of missing data, and the reduction of bias. In addition to gaining familiarity with the potential power of machine learning in aging, students will also explore the specific challenges and limitations inherent to these applications. By the end of the course, students will have a solid foundation in machine learning and its potential for advancing aging research and practice.
- Trainer/in: Charmayne Mary Lee Hughes
- Trainer/in: Mohamed Mehdi Ouerghi