Machine Learning

Oggetto:

Machine Learning

Oggetto:

Machine Learning

Oggetto:

Anno accademico 2019/2020

Codice dell'attività didattica

FIS0125

Docente

Prof. Piero Fariselli (Titolare del corso)

Corso di studi

Laurea Magistrale Interateneo in Fisica dei sistemi complessi

Anno

1° anno 2° anno

Periodo didattico

Terzo periodo didattico

Tipologia

D=A scelta dello studente

Crediti/Valenza

SSD dell'attività didattica

FIS/07 - fisica applicata (a beni culturali, ambientali, biologia e medicina)

Modalità di erogazione

Tradizionale

Lingua di insegnamento

Inglese

Modalità di frequenza

Facoltativa

Tipologia d'esame

Orale

Prerequisiti

Analisi matematica, algebra lineare, elementi di statistica e probabilità. Conoscenza di programmazione di base (meglio in Python).

Oggetto:

Il corso ha lo scopo di introdurre e appofondire algoritmi e modelli di apprendimento automatico (Machine Learning) per applicazioni interdisciplinari. Il corso si prefigge di fornire allo studente la capacità di utilizzare e sviluppare strumenti di machine-learning per l'analisi dei dati, con particolare riferimento ai modelli predittivi. Le lezioni teoriche si alterneranno ad esempi di codice. Lo studente sarà in grado di sviluppare pacchetti propri utilizzando librerie standard.

The objective of the course is to introduce algorithms and machine learning models for cross-disciplinary applications. The students will be able to analyze and model the data with particular emphasis on the predictive models. The theoretical lectures include example fo programming codes. At the end of the course, the students are expected to be able to write their packages using standard libraries.

Oggetto:

Risultati dell'apprendimento attesi

italiano
English

Compresione dei fondamenti teorici deli modelli di machine learning presentati e essere in grado di svilupparne dei nuovi. Conoscenza di alcune librerie Python per il machine learning e loro utilizzo.

The students are expected to possess appropriate knowledge of the theoretical aspects of the presented machine learning models and being able to design new ones. The students acquire expertise using some Python libraries.

Oggetto:

Modalità di insegnamento

italiano
English

Lezioni alla lavagna e con diapositive, ed esercizi al computer.

Lectures at the blackboard, slide projection and practical exercises with a computer.

Oggetto:

Modalità di verifica dell'apprendimento

italiano
English

Discussione alla lavagna, assegnazione e valutazione di semplici progetti di programmazione.

Discussion at the blackboard and assignment of programming projects.

Oggetto:

Attività di supporto

italiano
English

Oggetto:

Programma

italiano
English

Introduction to machine learning: relation with statistical learning, types of learning (supervised, unsupervised, reinforcement learning, collaborative filtering).
Introduction to simple predictive models: regression and classification.
A quick review of Python language.
Introduction to probabilistic models and learning algorithms: Markov models, hidden Markov models, maximum entropy models, conditional random fields. Graphical models as unifying picture. Introduction to causality.
Introduction to Neural Networks: feedforward, convolutional, recurrent.
Introduction to ensemble learning (bagging and boosting), multitask learning and multi-instance learning.
Elements of biological-inspired computations for optimization: genetic algorithms, ant colony optimization
The concept and examples of Reinforcement Learning.

Introduction to machine learning: relation with statistical learning, types of learning (supervised, unsupervised, reinforcement learning, collaborative filtering).
Introduction to simple predictive models: regression and classification.
A quick review of Python language.
Introduction to probabilistic models and learning algorithms: Markov models, hidden Markov models, maximum entropy models, conditional random fields. Graphical models as unifying picture. Introduction to causality.
Introduction to Neural Networks: feedforward, convolutional, recurrent.
Introduction to ensemble learning (bagging and boosting), multitask learning and multi-instance learning.
Elements of biological-inspired computations for optimization: genetic algorithms, ant colony optimization
The concept and examples of Reinforcement Learning.

Descrizione

Testi consigliati e bibliografia

Oggetto:

italiano
English

Mehta et al. A high-bias, low-variance introduction to Machine Learning for physicists. 2019. Phys Rep. 810:1-124 https://arxiv.org/abs/1803.08823
C.M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
A. Geron. Hands-On Machine Learning With Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. Oreilly & Associates Inc. 2017.
K.P. Machine Learning. A Probabilistic Perspective. MIT Press, 2012.
D. Simon. EVOLUTIONARY OPTIMIZATION ALGORITHMS. John Wiley & Sons, 2013.
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. 2nd edition. Springer, 2009.
I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.
R.S. Sutton and A.G. Barto. Reinforcement Learnin. An Introduction. MIT Press, 2017.
Andrew NG. Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning. deeplearning.ai, 2018

Mehta et al. A high-bias, low-variance introduction to Machine Learning for physicists. 2019. Phys Rep. 810:1-124 https://arxiv.org/abs/1803.08823
C.M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
A. Geron. Hands-On Machine Learning With Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. Oreilly & Associates Inc. 2017.
K.P. Machine Learning. A Probabilistic Perspective. MIT Press, 2012.
D. Simon. EVOLUTIONARY OPTIMIZATION ALGORITHMS. John Wiley & Sons, 2013.
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. 2nd edition. Springer, 2009.
I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.
R.S. Sutton and A.G. Barto. Reinforcement Learnin. An Introduction. MIT Press, 2017.
Andrew NG. Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning. deeplearning.ai 2018

Oggetto: