87994 - PATTERN RECOGNITION

Scheda insegnamento

  • Docente Gastone Castellani

  • Moduli Gastone Castellani (Modulo 1)
    Daniel Remondini (Modulo 2)
    (Modulo 3)

  • Crediti formativi 6

  • SSD FIS/07

  • Modalità didattica Convenzionale - Lezioni in presenza (Modulo 1)
    Convenzionale - Lezioni in presenza (Modulo 2)
    Convenzionale - Lezioni in presenza (Modulo 3)

  • Lingua di insegnamento Inglese

  • Campus di Bologna

  • Corso Laurea Magistrale in Physics (cod. 9245)

    Valido anche per Laurea Magistrale in Science of climate (cod. 5895)

SDGs

L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.

Salute e benessere Istruzione di qualità Parità di genere Imprese innovazione e infrastrutture

Anno Accademico 2022/2023

Conoscenze e abilità da conseguire

At the end of this course the student will learn the principles and commonly used paradigms and techniques of pattern recognition. In particular he/she will be able to: - explain and compare a variety of pattern classification techniques, - apply performance evaluation methods for pattern recognition, - apply pattern recognition techniques to real-world problems in the Applied Physics field, - implement simple pattern classifiers, - demonstrate successful applications to process and analyze data (e.g. images), and make automatic decisions based on extracted feature information.

Contenuti

24 hours (3 CFU) Castellani

Introduction Probability Theory, Probability densities, Expectations and covariances, Bayesian probabilities, Bayesian curve fitting, Model Selection, the Curse of Dimensionality, Statistical Inference and decision, loss functions for regression

Probability Distributions Binary and Multinomial Variables, Beta, Dirichlet and Gaussian Distribution, Gaussian Mixtures, the Exponential Family, maximum likelihood and sufficient statistics, Conjugate priors, Noninformative priors, Nonparametric Methods Inference and association test

Linear models for Regression and Classification Linear Basis Function Models, Bias-Variance Decomposition, Bayesian Linear Regression, Bayesian Model Comparison, Discriminant Functions, Probabilistic Generative and Discriminative Models

Neural Networks Learning rules, Hebbian,BCM and Hopfield model, Feed-forward Network Functions, Network Training, Error Backpropagation, Regularization,

Introduction to Kernel methods and Graphical models

24 hours (3 CFU) Remondini

Feature extraction & optimal projection:

Preprocessing: data regularization and normalization.

Principal Component Analysis, Singular Value Decomposition, Factor Analysis, Multi-Dimensional Scaling, ISOMAP, t-SNE, UMAP.

Relation to cost function maximization and eigenvalue problem (Rayleigh quotient).

Supervised classification:

Discriminant Analysis, Support Vector Machine, Random Forest.

Robust analysis procedures: crossvalidation.

Unsupervised clustering:

Hierarchical, k-means, spectral clustering techniques, DBscan.

Deep Learning:

DL Parameters and hyperparameters; training procedures.

Feedforward Neural Networks. Convolutional Neural Networks. Recurrent networks. LSTM, biLSTM & Transformer networks. Applications to imaging and NLP.

Testi/Bibliografia

Bishop - Machine learning and pattern recognition

Tibshirani Tusher - Methods of statistical learning

Metodi didattici

Slides and blackboard

Modalità di verifica e valutazione dell'apprendimento

roject, literature study and questions.

Strumenti a supporto della didattica

Programming environment and server connection

Orario di ricevimento

Consulta il sito web di Gastone Castellani

Consulta il sito web di Daniel Remondini

Consulta il sito web di