30557 - AI LAB
Course taught in English
Go to class group/s: 27
Class-group lessons delivered on campus
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 bio-informatics
- Bio-informatics batabases
- Individual projects: application of machine learning to real problems with a critical assessment of the results.
- Handle complex databases
- Apply different types of algorithms for data analysis: unsupervised clustering, supervised predictions, dimensional reduction.
- 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
The assesment will be based on the outcome of the group projects. A written report will be required for the group. The group members will have to give an oral presentation together at the exam session. Individual assessmet: each student will be asked to present and discuss their contribution to the project and the report.
Group project: 50%
Individual assessment: 50%
All data, instructions and study material will be provided during the course