MACHINE LEARNING AND BIG DATA ANALYTICS

Ingegneria Informatica MACHINE LEARNING AND BIG DATA ANALYTICS

0622700052
DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
EQF7
COMPUTER ENGINEERING
2018/2019



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2017
PRIMO SEMESTRE
CFUHOURSACTIVITY
324LESSONS
18EXERCISES
216LAB
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.
Prerequisites
THE COURSE REQUIRES BASIC KNOWLEDGE OF THE PYTHON PROGRAMMING LANGUAGE.
Contents
INTRODUCTION TO MACHINE LEARNING. THE GENERAL STRUCTURE OF A LEARNING SYSTEM.


STATISTICAL LEARNING. THEORETICAL FOUNDATIONS. BAYESIAN FORMULATION. BIAS AND VARIANCE ERRORS. THE CURSE OF DIMENSIONALITY. THE NAIVE BAYES CLASSIFIER. THE NEAREST NEIGHBOR CLASSIFIER.

FEATURE REDUCTION. PRINCIPAL COMPONENT ANALYSIS (PCA). LINEAR DISCRIMINANT ANALYSIS (LDA).

LINEAR REGRESSION. REGULARIZATION TECHNIQUES (RIDGE REGRESSION, LASSO). SUPPORT VECTOR MACHINES (SVM). THE KERNEL TRICK AND ITS USE FOR NON LINEARLY SEPARABLE PROBLEMS.

UNSUPERVISED LEARNING. CLUSTERING. K-MEANS, HIERARCHICAL CLUSTERING, DBSCAN.

LEARNING WITH ARTIFICIAL NEURAL NETWORKS. THE PERCEPTRON. MULTI-LAYER PERCEPTRONS (MLP).

CLUSTERING WITH NEURAL NETWORKS. SELF-ORGANIZING MAPS (SOM). LEARNING VECTOR QUANTIZATION (LVQ).

DEEP LEARNING WITH NEURAL NETWORKS. FUNDAMENTAL PRINCIPLES. CONVOLUTIONAL NEURAL NETWORKS (CNN). RECURRENT 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.
Texts
TO BE DEFINED
More Information
COURSE LANGUAGE IS ENGLISH.
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