30557 - AI LAB
Course taught in English
Go to class group/s: 27
Synchronous Blended: Lessons in synchronous mode in the classroom (for a maximum of one hour per credit in remote mode)
For a fruitful and effective learning experience, it is recommended a preliminary knowledge of basic linear algebra, elements of probability and statistics, calculus, optimization and programming (Python)
The purpose of the lab will be to apply basic machine learning techniques to real data. Students will be expected to tackle problems of bio-medical interest and learn how to extract relevant information from complex data. The projects will be preceded by an introduction to biomedical models, in order to be able to critically evaluate the results obtained.
- Elements of informatics for life sciences
- Biomedical and life sciences databases
- Individual projects: application of machine learning to real life science problems with a critical assessment of the results.
- Handle complex databases
- Apply different types of algorithms for data analysis: unsupervised clustering, dimensional reduction, supervised predictions.
- Evaluate performance based on domain knowledge and rigorous tests.
- approach the solution to data analysis problems coming from a real world context,
- use fundamental machine learning algorithms.
- critically evaluate the results
- Face-to-face lectures
- Exercises (exercises, database, software etc.)
- Lectures and exercises: Concepts in data analysis using machine learning to extract infomation from datasets of real world interest. The necessary domain specific knowledge will be provided.
- Group assignement: solve a real prediction problem
- Presentation of project as a group
A group project will be assigned to the students to verify they are able to:
- approach the solution to data analysis problems coming from a real world context in the best way
- use fundamental machine learning algorithms, selecting the best ones for the problem at hand
- handle complex databases
- critically evaluate the results based on domain knowledge and rigorous tests
The assesment will be based on the outcome of the group projects (50%) and on the contribution of each student to the project (50%).
For the group: the group must deliver a written final report and give an oral presentation together at the exam session. For each student: each student will be asked to present their part in detail and discuss their contribution to the project and to the report.
All data, instructions and study material will be provided during the course