Roberto TAGLIAFERRI | COMPUTATIONAL INTELLIGENCE
Roberto TAGLIAFERRI COMPUTATIONAL INTELLIGENCE
cod. 0222600019
COMPUTATIONAL INTELLIGENCE
| 0222600019 | |
| DIPARTIMENTO DI SCIENZE AZIENDALI - MANAGEMENT & INNOVATION SYSTEMS | |
| EQF7 | |
| BUSINESS INNOVATION AND INFORMATICS - BUSINESS, INNOVAZIONE ED INFORMATICA | |
| 2018/2019 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2016 | |
| SECONDO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 10 | 60 | LESSONS |
| Objectives | |
|---|---|
| (KNOWLEDGE AND UNDERSTANDING): THE STUDENT WILL ACQUIRE THE KNOWLEDGE OF MODELS OF MACHINE LEARNING , COMPUTATIONAL AND ARTIFICIAL INTELLIGENCE. (APPLYING KNOWLEDGE AND UNDERSTANDING) THE STUDENT WILL ACQUIRE THE COMPETENCE IN APPLYING ADVANCED MODELS OF MACHINE LEARNING , COMPUTATIONAL AND ARTIFICIAL INTELLIGENCE TO REAL PROBLEMS WITH MULTIDIMENSIONAL DATA IN THE AREA OF KNOWLEDGE DISCOVERY. (MAKING JUDGEMENTS) THE STUDENT WILL ACQUIRE THE CAPACITY OF ORIENTING HIMSELF IN THE STUDY AND RESOLUTION OF MULTIDIMENSIONAL DATA ANALYSIS AND MINING PROBLEMS. (COMMUNICATION SKILLS) THE STUDENT WILL ACQUIRE THE CAPACITY OF DESCRIBING, WITH APPROPRIATE LANGUAGE, THE CHARACTERISTICS OF MULTIDIMENSIONAL DATA ANALYSIS AND MINING PROBLEMS AND MODELS. |
| Prerequisites | |
|---|---|
| The basic concepts of mathematics, algorithms and data structures, probability and statistics, computer programming. |
| Contents | |
|---|---|
| I - PATTERN RECOGNITION AND MACHINE LEARNING I.A FIRST MODULE 1 INTRODUCTION TO PATTERN RECOGNITION (8 HOURS: LESSONS) 2 PDF DISTRIBUTIONS AND ESTIMATION (8 HOURS: 6 LESSONS AND 2 EXERCICES) 3 MIXTURE MODELS, CLUSTERING (6 HOURS: LESSONS) 4 PREPROCESSING, FEATURE EXTRACTION AND SELECTION, AND PCA (8 HOURS: LESSONS) I.B SECOND MODULE 5 DECISION TREES AND RANDOM FORESTS (4 HOURS: LESSONS) 6 LINEAR MODELS FOR REGRESSION AND CLASSIFICATION (6 HOURS: LESSONS) 7 NEURAL NETWORKS AND SVM FOR REGRESSION AND CLASSIFICATION (6 HOURS: LESSONS) 8 DEEP LEARNING (10 HOURS: 2 LESSONS AND 8 EXERCICES) II - INTRODUCTORY ELEMENTS OF INFORMATION THEORY (4 HOURS: LESSONS) |
| Teaching Methods | |
|---|---|
| THE COURSE IS DIVIDED INTO 2 MODULES, OF 30 HOURS OF LESSONS AND EXERCICES EACH ONE. LESSONS AND EXERCISES TO HELP THE STUDENT TO ACQUIRE THE THEORETICAL KNOWLEDGE AND THE APPLICATIVE COMPETENCES |
| Verification of learning | |
|---|---|
| ORAL SESSION INCLUDING THE PRESENTATION OF A PROJECT ON A TOPIC OF CI AND PR METHODS APPLIED TO DATA ANALYSIS PREVIOUSLY AGREED BETWEEN THE STUDENT AND THE DOCENT DURING THE COURSE. |
| Texts | |
|---|---|
| C.M. BISHOP: “PATTERN RECOGNITION AND MACHINE LEARNING”, SPRINGER SCIENCE, NEW YORK, 2006 R.O. DUDA, P.H. HART, D.G. STORK: “PATTERN CLASSIFICATION”, WILEY-INTERSCIENCE, II EDIZIONE, NEW YORK, 2001 I. GUYON, S. GUNN, M. NIKRAVESH, L.A. ZADEH: “FEATURE EXTRACTION: FOUNDATIONS AND APPLICATIONS”, SPRINGER, BERLINO, 2007 DAVID J. C. MACKAY: “INFORMATION THEORY, INFERENCE AND LEARNING ALGORITHMS”, CAMBRIDGE UNIVERSITY PRESS, CAMBRIDGE, 2003 SERGIOS THEODORIDIS, KONSTANTINOS KOUTROUMBAS: “PATTERN RECOGNITION”, 4TH EDITION. ACADEMIC PRESS, AMSTERDAM, 2008 SERGIOS THEODORIDIS AGGELOS PIKRAKIS KONSTANTINOS KOUTROUMBAS DIONISIS CAVOURAS: “INTRODUCTION TO PATTERN RECOGNITION: A MATLAB APPROACH”, ACADEMIC PRESS, AMSTERDAM, 2010 ROBERTO BATTITI, MAURO BRUNATO: "THE LION WAY. MACHINE LEARNING PLUS INTELLIGENT OPTIMIZATION. VERSION 2.0" LIONLAB, UNIVERSITY OF TRENTO, ITALY, 2014. FRANÇOIS CHOLLET: "DEEP LEARNING WITH PYTHON", MANNING PUBLICATIONS CO., SHELTER ISLAND, NY, 2018 |
| More Information | |
|---|---|
| THE COURSE FREQUENCY IS STRONGLY RECOMMENDED |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2019-10-21]
