Unit NEURAL NETWORKS AND LEARNING ALGORITHMS

Course
Electronic engineering for the internet-of-things
Study-unit Code
A003199
Curriculum
Industrial iot
Teacher
Renzo Perfetti
Teachers
  • Renzo Perfetti
Hours
  • 72 ore - Renzo Perfetti
CFU
9
Course Regulation
Coorte 2022
Offered
2022/23
Learning activities
Affine/integrativa
Area
Attività formative affini o integrative
Academic discipline
ING-IND/31
Type of study-unit
Opzionale (Optional)
Type of learning activities
Attività formativa monodisciplinare
Language of instruction
Italian
Contents
Introduction to neural networks and learning algorithms for classification and regression.
Reference texts
Duda, Hart , Stork, Pattern Classification, 2nd ed., Wiley

C.M. Bishop, Pattern Recognition and Machine Learning, Springer

James, Witten, Hastie, Tibshirani, An Introduction to Statistical Learning, Springer

S. Haykin, Neural Networks, Prentice-Hall

N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge
University Press
Educational objectives
Understanding of basic learning algorithms and of problems arising in their application and evaluation in pattern recognition and data analysis
Prerequisites
Probability and random variables. Linear algebra.
Teaching methods
Frontal lessons
Learning verification modality
Oral exam and essay
Extended program
Review on quadratic forms and random variables. Basic facts on machine learning.
Bayesian classification. Parametric and nonparametric methods. ML estimation. Gaussian mixtures. Expectation maximization. Linear separation. Perceptron. Steepest descent. LMS algorithm. Multiclass linear separation. Classification performance: overfitting, bias and variance, ROC, confusion matrix. Test and validation. Multilayer neural networks. Error backpropagation learning algorithm. Regolarization techniques. Hints on recurrent networks and deep learning. Support Vector Machines. Clustering algorithms. Self-organizing neural networks. Linear regression. Neural nets for regression.
Condividi su