Pasquale FOGGIA | MACHINE LEARNING

cod. 0622900024

MACHINE LEARNING

	0622900024
	DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
	EQF7
	DIGITAL HEALTH AND BIOINFORMATIC ENGINEERING
	2020/2021

PERCORSO COMUNE

	OBBLIGATORIO
	YEAR OF COURSE 1
	YEAR OF DIDACTIC SYSTEM 2018
	PRIMO SEMESTRE

TEACHING UNITS

SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
ING-INF/05	3	24	LESSONS	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS
ING-INF/05	3	24	EXERCISES	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS
ING-INF/05	3	24	LAB	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS

	Objectives
	THE COURSE IS AIMED AT PROVIDING THE STUDENT WITH THE THEORETICAL, METODOLOGICAL AND TECHNOLOGICAL KNOWLEDGE ON MACHINE LEARNING AND ON THE ANALYSIS OF LARGE DATA SETS, INCLUDING BOTH TRADITIONAL TECHNIQUES AND INNOVATIVE PARADIGMS SUCH AS DEEP LEARNING. KNOWLEDGE AND UNDERSTANDING PARADIGMS OF STRUCTURAL LEARNING, STATISTICAL LEARNING AND NEURAL LEARNING. UNSUPERVISED LEARNING. DEEP LEARNING. PARADIGMS AND TOOLS FOR BIG DATA ANALYSIS. APPLIED KNOWLEDGE AND UNDERSTANDING DESIGN AND REALIZATION OF SOLUTIONS TO LEARNING AND DATA ANALYTICS PROBLEM BY INTEGRATING EXISTING TOOLS AND TUNING IN AN EFFECTIVE WAY THEIR OPERATING PARAMETERS.

THE COURSE IS AIMED AT PROVIDING THE STUDENT WITH THE THEORETICAL, METODOLOGICAL AND TECHNOLOGICAL KNOWLEDGE ON MACHINE LEARNING AND ON THE ANALYSIS OF LARGE DATA SETS, INCLUDING BOTH TRADITIONAL TECHNIQUES AND INNOVATIVE PARADIGMS SUCH AS DEEP LEARNING.

KNOWLEDGE AND UNDERSTANDING
PARADIGMS OF STRUCTURAL LEARNING, STATISTICAL LEARNING AND NEURAL LEARNING. UNSUPERVISED LEARNING. DEEP LEARNING. PARADIGMS AND TOOLS FOR BIG DATA ANALYSIS.

APPLIED KNOWLEDGE AND UNDERSTANDING
DESIGN AND REALIZATION OF SOLUTIONS TO LEARNING AND DATA ANALYTICS PROBLEM BY INTEGRATING EXISTING TOOLS AND TUNING IN AN EFFECTIVE WAY THEIR OPERATING PARAMETERS.

	Prerequisites
	THE COURSE REQUIRES BASIC KNOWLEDGE OF THE PYTHON PROGRAMMING LANGUAGE.

	Contents
	Introduction to machine learning. Bias and variance errors. Overfitting. The curse of dimensionality. Bayesian and NN classifiers. Linear classifiers. SVM. Kernel trick. Linear regression and regularization techniques. Logistic regression. Clustering algorithms. Multiclassifier systems. Random forests. Reinforcement learning. Q-Learning. Introduction to Hidden Markov Models. Artificial Neural Networks. Perceptrons. MLP. Clustering with neural networks. LVQ. Manifold learning and Self Organizing Maps. Deep learning. Convolutional neural networks. Recurrent networks. LSTM and GRU. Advanced deep learning architectures. Autoencoders. Simultaneous detection and recognition. YOLO. Generative-Adversarial Networks.

Contents

Introduction to machine learning. Bias and variance errors. Overfitting. The curse of dimensionality.

Bayesian and NN classifiers.

Linear classifiers. SVM. Kernel trick. Linear regression and regularization techniques. Logistic regression.

Clustering algorithms.

Multiclassifier systems. Random forests.

Reinforcement learning. Q-Learning.

Introduction to Hidden Markov Models.

Artificial Neural Networks. Perceptrons. MLP.

Clustering with neural networks. LVQ. Manifold learning and Self Organizing Maps.

Deep learning. Convolutional neural networks.

Recurrent networks. LSTM and GRU.

Advanced deep learning architectures. Autoencoders. Simultaneous detection and recognition. YOLO. Generative-Adversarial Networks.

	Teaching Methods
	THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS.

	Verification of learning
	THE EXAM IS COMPOSED BY THE DISCUSSION OF A TEAM PROJECTWORK (FOR 3-4 PERSONS TEAMS) AND AN ORAL INTERVIEW. THE DISCUSSION OF THE PROJECTWORK AIMS AT EVALUATING THE ABILITY TO BUILD A SIMPLE APPLICATION OF THE TOOLS PRESENTED IN THE COURSE TO A PROBLEM ASSIGNED BY THE TEACHER, AND INCLUDES A PRACTICAL DEMONSTRATION OF THE REALIZED APPLICATION, A PRESENTATION OF A QUANTITATIVE EVALUATION OF THE APPLICATION PERFORMANCE AND A DESCRIPTION OF THE TECHNICAL CHOICES INVOLVED IN ITS REALIZATION. THE INTERVIEW EVALUATES THE LEVEL OF THE KNOWLEDGE AND UNDERSTANDING OF THE THEORETICAL TOPICS, TOGETHER WITH THE EXPOSITION ABILITY OF THE CANDIDATE.

Verification of learning

THE EXAM IS COMPOSED BY THE DISCUSSION OF A TEAM PROJECTWORK (FOR 3-4 PERSONS TEAMS) AND AN ORAL INTERVIEW. THE DISCUSSION OF THE PROJECTWORK AIMS AT EVALUATING THE ABILITY TO BUILD A SIMPLE APPLICATION OF THE TOOLS PRESENTED IN THE COURSE TO A PROBLEM ASSIGNED BY THE TEACHER, AND INCLUDES A PRACTICAL DEMONSTRATION OF THE REALIZED APPLICATION, A PRESENTATION OF A QUANTITATIVE EVALUATION OF THE APPLICATION PERFORMANCE AND A DESCRIPTION OF THE TECHNICAL CHOICES INVOLVED IN ITS REALIZATION. THE INTERVIEW EVALUATES THE LEVEL OF THE KNOWLEDGE AND UNDERSTANDING OF THE THEORETICAL TOPICS, TOGETHER WITH THE EXPOSITION ABILITY OF THE CANDIDATE.

	Texts
	Lecture notes and other material provided during the course

	More Information
	COURSE LANGUAGE IS ENGLISH.

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Pasquale FOGGIA | MACHINE LEARNING

MACHINE LEARNING

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Pasquale FOGGIA | MACHINE LEARNING