Syllabus del corso
Obiettivi formativi
Fornire sia le conoscenze sia le capacità di utilizzare algoritmi di Machine Learning e di applicarli alla soluzione di probemi. Lo studente acquisirà padronanza delle metodologie di machine learning per affrontare problemi di classificazione e regressione. acquisirà la capacità critica necessaria per valutare quali problemi possono essere risolti con tecniche di machine learning.
Conoscenza e comprensione. Questo insegnamento fornirà conoscenze e capacità di comprensione relativamente a: algoritmi di machine learning, categorie di problemi risolvibili con algoritmi di machine learning, principali metodologie applicabili e criticità a cui prestare attenzione.
Capacità di applicare conoscenza e comprensione. Alla fine dell'insegnamento gli studenti saranno in grado di: applicare algoritmi di machine learning per affrontare problemi di classificazione, regressione ed in generale per estrarre informazioni dai dati. Saranno in grado di svolgere sia dei test di fattibilità sia di realizzare soluzioni complete.
Contenuti sintetici
- Statistical methods for machine learning
- Beyong linear models
- Feature Engineering and Machine Learning Algorithms Tuning
- Artificial Neural Networks and Deep Learning
Programma esteso
- Statistical methods for machine
learning
- Supervised and unsupervised learning
- Recall to regression analysis
- Classification analysis
- Cross validation and bootstrap
- Model selection and regularization
- Beyond linear models
- Tree-based methods
- Support vector machines
- Feature Engineering and Machine
Learning Algorithms Tuning
- Feature Engineering and Selection (Bag of Words, Embeddings, Tensors, ...)
- Data Observability and Model existence issues
- Hyperparameters optimization (Grid-Search, Random-Search, Advanced Search methodologies)
- Artificial Neural Networks and Deep
Learning
- Artificial Neural Networks (ANNs) and Feed Forward Neural Network introduction
- Training Algorithm: Gradient Descent, Optimization Methodology
- Deep learning and Artificial Neural Networks types (Fully Connected networks, Feed Forward networks, Convolutional networks, Recurrent networks, …)
- Industrial applications and open research issues
The teachers may decide to change the program or to focus on specific topics based on the students' previous knowledge.
Prerequisiti
Linear Algebra, Aver frequentato un corso di statistica descrittiva e inferenziale, Aver frequentato un corso di programmazione.
Desiderabili, ma non necessarie: conoscenze del linguaggio Python e del linguaggio R
Metodi didattici
Lezione in laboratorio informatico
Modalità di verifica dell'apprendimento
Esame orale. Potranno essere concordati dei progetti sostitutivi di parte dell’esame orale.
Testi di riferimento
Saranno comunicati dal docente all’inizio della lezione.
Gareth James, Daniela Wittens, Trevor Hastie and Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer. Available at http://www-bcf.usc.edu/~gareth/ISL/
C.M. Bishop (2006), Pattern Recognition and Machine Learning. Springer (New York)
Periodo di erogazione dell’insegnamento
3. Ciclo (1. parte del 2. Semestre)
Lingua di insegnamento
Inglese
Learning objectives
To gain knowledge about Machine Learning algorithms, and to apply them to solve problems. The student will master machine learning methodologies to deal with classification and regression problems. The student will acquire the critical thinking to evaluate which problems can be solved using machine learning techniques.
Knowledge and understanding. This course will provide knowledge and understanding about: machine learning algorithms; machine learning problem categories and the algorithms best suited for their solutions; main methodologies and related issues.
Ability to apply knowledge and understanding. At the end of the course, students will be able to: apply machine learning algorithms to tackle classification, regression problems, and to extract information from data. The students will be able to design and implement both feasibility tests and complete solutions.
Contents
- Statistical methods for machine learning
- Beyong linear models
- Feature Engineering and Machine Learning Algorithms Tuning
- Artificial Neural Networks and Deep Learning
Detailed program
- Statistical methods for machine learning
- Supervised and unsupervised learning
- Recall to regression analysis
- Classification analysis
- Cross validation and bootstrap
- Model selection and regularization
- Beyond linear models
- Tree-based methods
- Support vector machines
- Feature Engineering and Machine Learning Algorithms Tuning
- Feature Engineering and Selection (Bag of Words, Embeddings, Tensors, ...)
- Data Observability and Model existence issues
- Hyperparameters optimization (Grid-Search, Random-Search, Advanced Search methodologies)
- Artificial Neural Networks and Deep Learning
- Artificial Neural Networks (ANNs) and Feed Forward Neural Network introduction
- Training Algorithm: Gradient Descent, Optimization Methodology
- Deep learning and Artificial Neural Networks types (Fully Connected networks, Feed Forward networks, Convolutional networks, Recurrent networks, …)
- Industrial applications and open research issues
The teachers may decide to change the program or to focus on specific topics based on the students' previous knowledge.
Prerequisites
Linear Algebra, Foundation of descriptive and inferential statistics, Foundation of coding (knowledge of a programming language).
Nice to have: Python language and the R language knowledge
Teaching methods
Lectures will be given in a computer laboratory
Assessment methods
Oral examination. The student may partially replace the examination with a project (to be agreed in advance with the teachers)
Textbooks and Reading Materials
Further information will be given during the first lesson.
Gareth James, Daniela Wittens, Trevor Hastie and Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer. Available at http://www-bcf.usc.edu/~gareth/ISL/
C.M. Bishop (2006), Pattern Recognition and Machine Learning. Springer (New York)
Semester
3rd Cycle (1st part of the 2. Semester)
Teaching language
English
Staff
-
Mirko Cesarini
-
Stefano Peluso