Scheda insegnamento
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Docente Daniele Bonacorsi
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Crediti formativi 4
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SSD FIS/01
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Modalità didattica Convenzionale - Lezioni in presenza
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Lingua di insegnamento Inglese
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Campus di Bologna
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Corso Laurea Magistrale in Bioinformatics (cod. 8020)
Anno Accademico 2019/2020
Conoscenze e abilità da conseguire
At the end of the course the student is able to handle different Machine Learning and Deep Learning models, to tune them to specific applications, and to design approaches that may scale with large amount of data
Contenuti
Part 1 - Scratching the surface of the box
- Real-worlds applications: examples of current and (possible) future outcomes of ML applications
- Basic foundations of AI, ML, DL (with a focus on ML at large)
- Mathematical models (classification, regression, clustering, dimensionality reduction, parametric vs non-parametric models)
- Supervised vs unsupervised vs reinforcement learning
- Loss functions, gradient descent
- Linear regression, logistic regression
- Overfitting, regularization, training procedures and cross validation
- Neural networks, feed forward NN, multi-layer NN, regularization, activation functions, deep NN, decision trees and ensemble methods, random forest.
- Elements of unsupervised learning
- Practical advices: what approach to choose for your problem, how to debug your learning, cheat sheet of potential fixes
- Hardwar: the right infrastructure for the right ML strategy tailored to your problem
- applications of ML techniques to real-time examples (e.g. High-Energy Physics at the LHC accelerator at CERN)
- overview of (selected) frameworks for ML
- hands-on on scikit-learn, tensorflow, keras, pytorch
Testi/Bibliografia
Course material will be shared, plus external MOOCs and books will be suggested during the course.
Metodi didattici
Intuitions on the theory will be offered, and practical hands-on sessions on all ML frameworks and librarires, with real-world examples, will be held with Python and Jupyter notebook.
Modalità di verifica e valutazione dell'apprendimento
A final end-to-end project on ML on a real dataset of interest.
Strumenti a supporto della didattica
Slides for the theory, Python-based Jupyter notebooks for the exercises/tutorials.
Orario di ricevimento
Consulta il sito web di Daniele Bonacorsi