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.
At the end of the course students will be able to undesrtand how to:
- 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.
At the end of the course students will be able to:
- 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.)
- Individual assignments
- Exercises: implement different algoritms for data analysis, using machine learning software and extracting infomation from datasets of real world interest.
- Individual assignement: solve a real prediction problem
The assesment will be based on the outcome of the individual and the group projects.
A written report will be required for each student.
One student for each group project will have give an oral presentation in lcass.
Individual project: 50%
Goup project: 50%
The teachning material will be fully provided by the teacher at the beginning of the course.