LOREDANA CARUCCIO | MACHINE LEARNING

cod. 0512100064

MACHINE LEARNING

	0512100064
	COMPUTER SCIENCE
	EQF6
	COMPUTER SCIENCE
	2024/2025

PERCORSO COMUNE

	YEAR OF COURSE 3
	YEAR OF DIDACTIC SYSTEM 2017
	AUTUMN SEMESTER

TEACHING UNITS

SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
INF/01	4	32	LESSONS	SUPPLEMENTARY COMPULSORY SUBJECTS
INF/01	2	16	LAB	SUPPLEMENTARY COMPULSORY SUBJECTS

PROFESSORS

	GIUSEPPE POLESE T Curriculum
	LOREDANA CARUCCIO Curriculum

EXAMS

Exam	Date	Session
APPELLO PROF. POLESE	08/01/2025 - 09:00	SESSIONE ORDINARIA
APPELLO PROF. POLESE	08/01/2025 - 09:00	SESSIONE DI RECUPERO
APPELLO PROF. POLESE	04/02/2025 - 09:00	SESSIONE ORDINARIA
APPELLO PROF. POLESE	04/02/2025 - 09:00	SESSIONE DI RECUPERO

	Objectives
	THE MACHINE LEARNING COURSE PROVIDES THE METHODOLOGICAL AND TECHNOLOGICAL MEANS TO DESIGN AND IMPLEMENT MACHINE LEARNING SYSTEMS STARTING FROM HETEROGENEOUS DATA, INCLUDING SENSOR DATA, TO BE RELEASED ON SEVERAL TYPES OF PLATFORMS. KNOWLEDGE AND UNDERSTANDING THE STUDENT IS EXPECTED TO ACQUIRE: • FUNDAMENTAL CONCEPTS CONCERNING THE PREPARATION OF TRAINING DATA; • KNOWLEDGE ABOUT THE MAIN APPLICATION DOMAINS OF MACHINE LEARNING; • KNOWLEDGE ABOUT THE MAIN MACHINE LEARNING MODELS AND ALGORITHMS; • CONCEPTS CONCERNING ETHICAL ASPECTS RELATED TO THE USE OF MACHINE LEARNING SYSTEMS. APPLYING KNOWLEDGE AND UNDERSTANDING THE STUDENT IS EXPECTED TO BE ABLE TO: • ANALYZE REAL PROBLEMS AND SELECT THE MOST SUITABLE MACHINE LEARNING TECHNIQUES TO SOLVE THEM, ALSO DEVELOPING THE CAPABILITY TO IMPLEMENT THEM; • EVALUATE WHETHER MACHINE LEARNING TECHNIQUES ARE SUITABLE FOR SOLVING A GIVEN PROBLEM; • SELECT THE MOST SUITABLE MODELS AND ALGORITHMS AND APPLY THE NECESSARY METHODOLOGICAL STEPS TO FORMULATE AND IMPLEMENT A SUITABLE SOLUTION FOR A GIVEN PROBLEM; • RAPIDLY FAMILIARIZE WITH NEW MACHINE LEARNING TOPICS AND INTERACT WITH COMMUNITIES OF DEVELOPERS WITH EXPERTISE ON SUCH TOPICS. AUTONOMY OF JUDGMENT THE STUDENT WILL GAIN AUTONOMY OF JUDGMENT AS S/HE IS EXPECTED TO BE ABLE TO: • STUDY THEORETICAL CONCEPTS AND APPLY THEM TO VARIOUS APPLICATION DOMAINS THROUGH EXERCISES TO ASSESS AND POSSIBLY IMPROVE THE ACHIEVED LEVEL OF UNDERSTANDING; • DEVELOP AN APPLICATION PROJECT IN ALL ITS PHASES, LEARNING TO MAKE INDEPENDENT DESIGN CHOICES, BASED ON THE GUIDELINES PROVIDED DURING THE COURSE, AND FINALLY JUDGE THE QUALITY OF THE ARTIFACTS PRODUCED THROUGH APPROPRIATE TESTS; • EVALUATE THE PROGRESS OF THE PROJECT DEVELOPMENT PHASES, BOTH ACCORDING TO THE GOALS SET AND THE DEADLINES TO BE MET POSSIBLY SIMULATING THE DEVELOPMENT DYNAMICS IN REAL CONTEXTS. COMMUNICATION SKILLS THE STUDENT IS EXPECTED TO BE ABLE TO: • DESCRIBE, DISCUSS, AND JUSTIFY THE DECISIONS MADE DURING THE DESIGN AND DEVELOPMENT PHASES THROUGH APPROPRIATE PROJECT DOCUMENTATION; • COMMUNICATE THE FUNCTIONAL OBJECTIVES, THE DELIVERED ARTIFACTS, AND IMPLEMENTATION DETAILS OF THE DEVELOPED APPLICATION. LEARNING SKILLS THE STUDENT IS EXPECTED TO BE ABLE TO: • ACQUIRE THE ABILITY TO STUDY THE CHARACTERISTICS OF SEVERAL TRAINING MODELS, LEARNING TO COMPARE THEM AND SELECT THOSE MOST SUITABLE FOR A GIVEN PROBLEM; • ACQUIRE THE CAPABILITY TO EVALUATE THE PERFORMANCES OF A GIVEN SOLUTION AND TO CONCEIVE THE NECESSARY TUNING ACTIVITIES TO IMPROVE THEM.

THE MACHINE LEARNING COURSE PROVIDES THE METHODOLOGICAL AND TECHNOLOGICAL MEANS TO DESIGN AND IMPLEMENT MACHINE LEARNING SYSTEMS STARTING FROM HETEROGENEOUS DATA, INCLUDING SENSOR DATA, TO BE RELEASED ON SEVERAL TYPES OF PLATFORMS.

KNOWLEDGE AND UNDERSTANDING
THE STUDENT IS EXPECTED TO ACQUIRE:
• FUNDAMENTAL CONCEPTS CONCERNING THE PREPARATION OF TRAINING DATA;
• KNOWLEDGE ABOUT THE MAIN APPLICATION DOMAINS OF MACHINE LEARNING;
• KNOWLEDGE ABOUT THE MAIN MACHINE LEARNING MODELS AND ALGORITHMS;
• CONCEPTS CONCERNING ETHICAL ASPECTS RELATED TO THE USE OF MACHINE LEARNING SYSTEMS.

APPLYING KNOWLEDGE AND UNDERSTANDING
THE STUDENT IS EXPECTED TO BE ABLE TO:
• ANALYZE REAL PROBLEMS AND SELECT THE MOST SUITABLE MACHINE LEARNING TECHNIQUES TO SOLVE THEM, ALSO DEVELOPING THE CAPABILITY TO IMPLEMENT THEM;
• EVALUATE WHETHER MACHINE LEARNING TECHNIQUES ARE SUITABLE FOR SOLVING A GIVEN PROBLEM;
• SELECT THE MOST SUITABLE MODELS AND ALGORITHMS AND APPLY THE NECESSARY METHODOLOGICAL STEPS TO FORMULATE AND IMPLEMENT A SUITABLE SOLUTION FOR A GIVEN PROBLEM;
• RAPIDLY FAMILIARIZE WITH NEW MACHINE LEARNING TOPICS AND INTERACT WITH COMMUNITIES OF DEVELOPERS WITH EXPERTISE ON SUCH TOPICS.

AUTONOMY OF JUDGMENT
THE STUDENT WILL GAIN AUTONOMY OF JUDGMENT AS S/HE IS EXPECTED TO BE ABLE TO:
• STUDY THEORETICAL CONCEPTS AND APPLY THEM TO VARIOUS APPLICATION DOMAINS THROUGH EXERCISES TO ASSESS AND POSSIBLY IMPROVE THE ACHIEVED LEVEL OF UNDERSTANDING;
• DEVELOP AN APPLICATION PROJECT IN ALL ITS PHASES, LEARNING TO MAKE INDEPENDENT DESIGN CHOICES, BASED ON THE GUIDELINES PROVIDED DURING THE COURSE, AND FINALLY JUDGE THE QUALITY OF THE ARTIFACTS PRODUCED THROUGH APPROPRIATE TESTS;
• EVALUATE THE PROGRESS OF THE PROJECT DEVELOPMENT PHASES, BOTH ACCORDING TO THE GOALS SET AND THE DEADLINES TO BE MET POSSIBLY SIMULATING THE DEVELOPMENT DYNAMICS IN REAL CONTEXTS.

COMMUNICATION SKILLS
THE STUDENT IS EXPECTED TO BE ABLE TO:
• DESCRIBE, DISCUSS, AND JUSTIFY THE DECISIONS MADE DURING THE DESIGN AND DEVELOPMENT PHASES THROUGH APPROPRIATE PROJECT DOCUMENTATION;
• COMMUNICATE THE FUNCTIONAL OBJECTIVES, THE DELIVERED ARTIFACTS, AND IMPLEMENTATION DETAILS OF THE DEVELOPED APPLICATION.

LEARNING SKILLS
THE STUDENT IS EXPECTED TO BE ABLE TO:
• ACQUIRE THE ABILITY TO STUDY THE CHARACTERISTICS OF SEVERAL TRAINING MODELS, LEARNING TO COMPARE THEM AND SELECT THOSE MOST SUITABLE FOR A GIVEN PROBLEM;
• ACQUIRE THE CAPABILITY TO EVALUATE THE PERFORMANCES OF A GIVEN SOLUTION AND TO CONCEIVE THE NECESSARY TUNING ACTIVITIES TO IMPROVE THEM.

	Prerequisites
	STUDENTS SHOULD BE FAMILIAR WITH FUNDAMENTALS OF DATA MANAGEMENT AND A PROGRAMMING LANGUAGE.

	Contents
	THE COURSE WILL FOCUS ON THE FOLLOWING TOPICS: •INTRODUCTION TO MACHINE LEARNING AND TO THE MACHINE LEARNING SYSTEM DESIGN (4 HOURS) -REQUIREMENTS FOR ML SYSTEMS (1 HOUR) -PIPELINE FOR THE DESIGN OF A ML SYSTEM (1 HOUR) -MACHINE LEARNING APPROACHES (1 HOUR) -ML ISSUES: UNDERFITTING AND OVERFITTING (1 HOUR) •DATA ENGINEERING (6 HOURS) -DATA SOURCES (1 HOUR) -DATA FORMATS (1 HOUR) -STRUCTURED AND UNSTRUCTURED DATA (1 HOUR) -BATCH VS STREAM PROCESSING (1 HOUR) -OVERVIEW ON NOSQL DATABASES (2 HOURS) • TRAINING DATA (4 HOURS) -SAMPLING (1 HOUR) -LABELING (1 HOUR) -CLASS IMBALANCE (1 HOUR) -DATA AUGMENTATION (1 HOUR) •FEATURE ENGINEERING (4 HOURS) -LEARNED FEATURES VERSUS ENGINEERED FEATURES (1 HOUR) -COMMON FEATURE ENGINEERING OPERATIONS (1 HOUR) -HANDLING MISSING VALUES (1 HOUR) -SCALING (1 HOUR) •DEVELOPMENT AND EVALUATION OF MACHINE LEARNING MODELS (8 HOURS) -EVALUATING ML MODELS (2 HOURS) -ENSEMBLE LEARNING (1 HOUR) -DISTRIBUTED TRAINING (2 HOURS) -AUTOML (1 HOUR) -MODEL OFFLINE EVALUATION (2 HOURS) •OVERVIEW ON MACHINE LEARNING MODELS (4 HOURS) •OVERVIEW ON ETHICAL ASPECTS OF MACHINE LEARNING (2 HOURS) -MANAGEMENT OF UNCORRECT PREDICTIONS (1 HOUR) -RESPONSIBLE AI (1 HOUR) LABORATORY •INTRODUCTION TO THE PYTHON LANGUAGE (2 HOURS) •PYTHON LIBRARIES FOR THE ML (2 HOURS) •SCIKIT-LEARN (2 HOURS) •ML PIPELINE DEFINITION: PRACTICAL APPLCATIONS (10 HOURS)

Contents

THE COURSE WILL FOCUS ON THE FOLLOWING TOPICS:

•INTRODUCTION TO MACHINE LEARNING AND TO THE MACHINE LEARNING SYSTEM DESIGN (4 HOURS)
-REQUIREMENTS FOR ML SYSTEMS (1 HOUR)
-PIPELINE FOR THE DESIGN OF A ML SYSTEM (1 HOUR)
-MACHINE LEARNING APPROACHES (1 HOUR)
-ML ISSUES: UNDERFITTING AND OVERFITTING (1 HOUR)

•DATA ENGINEERING (6 HOURS)
-DATA SOURCES (1 HOUR)
-DATA FORMATS (1 HOUR)
-STRUCTURED AND UNSTRUCTURED DATA (1 HOUR)
-BATCH VS STREAM PROCESSING (1 HOUR)
-OVERVIEW ON NOSQL DATABASES (2 HOURS)

• TRAINING DATA (4 HOURS)
-SAMPLING (1 HOUR)
-LABELING (1 HOUR)
-CLASS IMBALANCE (1 HOUR)
-DATA AUGMENTATION (1 HOUR)

•FEATURE ENGINEERING (4 HOURS)
-LEARNED FEATURES VERSUS ENGINEERED FEATURES (1 HOUR)
-COMMON FEATURE ENGINEERING OPERATIONS (1 HOUR)
-HANDLING MISSING VALUES (1 HOUR)
-SCALING (1 HOUR)

•DEVELOPMENT AND EVALUATION OF MACHINE LEARNING MODELS (8 HOURS)
-EVALUATING ML MODELS (2 HOURS)
-ENSEMBLE LEARNING (1 HOUR)
-DISTRIBUTED TRAINING (2 HOURS)
-AUTOML (1 HOUR)
-MODEL OFFLINE EVALUATION (2 HOURS)

•OVERVIEW ON MACHINE LEARNING MODELS (4 HOURS)

•OVERVIEW ON ETHICAL ASPECTS OF MACHINE LEARNING (2 HOURS)
-MANAGEMENT OF UNCORRECT PREDICTIONS (1 HOUR)
-RESPONSIBLE AI (1 HOUR)

LABORATORY

•INTRODUCTION TO THE PYTHON LANGUAGE (2 HOURS)
•PYTHON LIBRARIES FOR THE ML (2 HOURS)
•SCIKIT-LEARN (2 HOURS)
•ML PIPELINE DEFINITION: PRACTICAL APPLCATIONS (10 HOURS)

	Teaching Methods
	THE COURSE INCLUDES 32 HOURS OF LECTURES ON THEORETICAL TOPICS AND 16 HOURS OF LABORATORY LECTURES ON TOOLS AND APPLICATIONS, AIMING TO INTRODUCE CONCEPTS AND TO DEVELOP ABILITIES TO DESIGN AND IMPLEMENT SOLUTIONS FOR PROBLEMS LENDING THEMSELVES TO THE USE OF MACHINE LEARNING TECHNIQUES. COURSE CONTENTS ARE PRESENTED THROUGH POWERPOINT SLIDES, STIMULATING CRITICAL DISCUSSIONS WITH THE STUDENTS. FOR EACH PRESENTED TOPIC, THE INSTRUCTOR WILL ILLUSTRATE POTENTIAL TASKS ON WHICH A STUDENT OR A GROUP CAN DEVELOP THE COURSE PROJECT. AS FOR LANGUAGES AND TOOLS, OTHER THAN POWERPOINT SLIDES, THROUGH WHICH CONCEPTS AND POSSIBLE ADDITIONAL RESOURCES, SUCH AS LINKS TO FORUMS, MANUALS, AND OTHER SITES ARE PRESENTED, DURING OFFICE HOURS STUDENTS ARE GIVEN THE POSSIBILITY TO ASK SUPPORT ON SIMULATIONS THEY PERFORMED ON THEIR PERSONAL COMPUTER, TO ASK CLARIFICATIONS, AND SOLVE POSSIBLE TECHNICAL PROBLEMS WITH THE ASSISTANCE OF THE INSTRUCTOR.

Teaching Methods

THE COURSE INCLUDES 32 HOURS OF LECTURES ON THEORETICAL TOPICS AND 16 HOURS OF LABORATORY LECTURES ON TOOLS AND APPLICATIONS, AIMING TO INTRODUCE CONCEPTS AND TO DEVELOP ABILITIES TO DESIGN AND IMPLEMENT SOLUTIONS FOR PROBLEMS LENDING THEMSELVES TO THE USE OF MACHINE LEARNING TECHNIQUES. COURSE CONTENTS ARE PRESENTED THROUGH POWERPOINT SLIDES, STIMULATING CRITICAL DISCUSSIONS WITH THE STUDENTS. FOR EACH PRESENTED TOPIC, THE INSTRUCTOR WILL ILLUSTRATE POTENTIAL TASKS ON WHICH A STUDENT OR A GROUP CAN DEVELOP THE COURSE PROJECT. AS FOR LANGUAGES AND TOOLS, OTHER THAN POWERPOINT SLIDES, THROUGH WHICH CONCEPTS AND POSSIBLE ADDITIONAL RESOURCES, SUCH AS LINKS TO FORUMS, MANUALS, AND OTHER SITES ARE PRESENTED, DURING OFFICE HOURS STUDENTS ARE GIVEN THE POSSIBILITY TO ASK SUPPORT ON SIMULATIONS THEY PERFORMED ON THEIR PERSONAL COMPUTER, TO ASK CLARIFICATIONS, AND SOLVE POSSIBLE TECHNICAL PROBLEMS WITH THE ASSISTANCE OF THE INSTRUCTOR.

	Verification of learning
	THE ACHIEVEMENT OF THE COURSE OBJECTIVES IS CERTIFIED BY MEANS OF AN EXAM, WHOSE FINAL GRADE IS EXPRESSED ON A SCALE OF 30. THE EXAM CONSISTS OF A WRITTEN TEST (ALTERNATIVELY, A MIDTERM WRITTEN TEST) AND AN ORAL EXAMINATION. THE PROJECT AIMS TO ASSESS THE ABILITY TO APPLY THE ACQUIRED KNOWLEDGE. IT CAN BE CARRIED OUT INDIVIDUALLY OR IN TEAMS OF UP TO 3 STUDENTS, CHOOSING FROM A RANGE OF PROPOSALS PROVIDED BY THE INSTRUCTOR. DURING THE PROJECT DEVELOPMENT, STUDENTS CAN INTERACT WITH THE INSTRUCTOR IN ORDER TO COMMUNICATE THE PROJECT’S PROGRESS AND POSSIBLE CRITICAL ISSUES, DEBATING ON THE GOALS OF THE PROJECT AND THE MODALITIES TO CONTINUE IT. AT THE END OF THE PROJECT, STUDENTS MUST DELIVER A TECHNICAL REPORT CONTAINING THE PROJECT DOCUMENTATION, AND A POWERPOINT PRESENTATION OF THE PROJECT, LASTING ABOUT 30 MINUTES. AT THE END OF THE PROJECT PRESENTATION, STUDENTS MUST UNDERGO AN INDIVIDUAL ORAL EXAM. IT CONSISTS OF AN INTERVIEW WITH QUESTIONS ON THE THEORETICAL AND METHODOLOGICAL CONTENTS TAUGHT DURING THE COURSE, AIMING TO ASSESS THE LEVEL OF KNOWLEDGE AND UNDERSTANDING, AS WELL AS THE ABILITY TO EXPOSE CONCEPTS. ORAL EXAMINATION CAN BE CARRIED OUT ON THE SAME DATE OF THE PROJECT PRESENTATION, OR ON ANOTHER DATE THAT INDIVIDUAL MEMBERS OF THE PROJECT GROUP CAN SELECT WITH THE TEACHER. GENERALLY, THE FINAL GRADE AVERAGES SCORES IN THIRTIETHS OF THE WRITTEN EXAMINATION, THE PROJECT AND ORAL EXAMINATION.

Verification of learning

THE ACHIEVEMENT OF THE COURSE OBJECTIVES IS CERTIFIED BY MEANS OF AN EXAM, WHOSE FINAL GRADE IS EXPRESSED ON A SCALE OF 30. THE EXAM CONSISTS OF A WRITTEN TEST (ALTERNATIVELY, A MIDTERM WRITTEN TEST) AND AN ORAL EXAMINATION. THE PROJECT AIMS TO ASSESS THE ABILITY TO APPLY THE ACQUIRED KNOWLEDGE. IT CAN BE CARRIED OUT INDIVIDUALLY OR IN TEAMS OF UP TO 3 STUDENTS, CHOOSING FROM A RANGE OF PROPOSALS PROVIDED BY THE INSTRUCTOR. DURING THE PROJECT DEVELOPMENT, STUDENTS CAN INTERACT WITH THE INSTRUCTOR IN ORDER TO COMMUNICATE THE PROJECT’S PROGRESS AND POSSIBLE CRITICAL ISSUES, DEBATING ON THE GOALS OF THE PROJECT AND THE MODALITIES TO CONTINUE IT. AT THE END OF THE PROJECT, STUDENTS MUST DELIVER A TECHNICAL REPORT CONTAINING THE PROJECT DOCUMENTATION, AND A POWERPOINT PRESENTATION OF THE PROJECT, LASTING ABOUT 30 MINUTES. AT THE END OF THE PROJECT PRESENTATION, STUDENTS MUST UNDERGO AN INDIVIDUAL ORAL EXAM. IT CONSISTS OF AN INTERVIEW WITH QUESTIONS ON THE THEORETICAL AND METHODOLOGICAL CONTENTS TAUGHT DURING THE COURSE, AIMING TO ASSESS THE LEVEL OF KNOWLEDGE AND UNDERSTANDING, AS WELL AS THE ABILITY TO EXPOSE CONCEPTS. ORAL EXAMINATION CAN BE CARRIED OUT ON THE SAME DATE OF THE PROJECT PRESENTATION, OR ON ANOTHER DATE THAT INDIVIDUAL MEMBERS OF THE PROJECT GROUP CAN SELECT WITH THE TEACHER.
GENERALLY, THE FINAL GRADE AVERAGES SCORES IN THIRTIETHS OF THE WRITTEN EXAMINATION, THE PROJECT AND ORAL EXAMINATION.

	Texts
	1.CHIP HUYEN, DESIGNING MACHINE LEARNING SYSTEMS - AN ITERATIVE PROCESS FOR PRODUCTION-READY APPLICATIONS, O’REILLY (2022). 2.ANDREAS LINDHOLM, NIKLAS WAHLSTRÖM, FREDRIK LINDSTEN, THOMAS B. SCHÖN, MACHINE LEARNING - A FIRST COURSE FOR ENGINEERS AND SCIENTISTS CAMBRIDGE UNIVERSITY PRESS (2022). 3.HAYDEN LIU, PYTHON MACHINE LEARNING BY EXAMPLE – PACKT (2022) – THIRD EDITION. 4.JOHN V. GUTTAG, INTRODUCTION TO COMPUTATION AND PROGRAMMING USING PYTHON WITH APPLICATION TO COMPUTATIONAL MODELING AND UNDERSTANDING DATA – MIT PRESS (2021) – THIRD EDITION.

Texts

1.CHIP HUYEN, DESIGNING MACHINE LEARNING SYSTEMS - AN ITERATIVE PROCESS FOR PRODUCTION-READY APPLICATIONS, O’REILLY (2022).

2.ANDREAS LINDHOLM, NIKLAS WAHLSTRÖM, FREDRIK LINDSTEN, THOMAS B. SCHÖN, MACHINE LEARNING - A FIRST COURSE FOR ENGINEERS AND SCIENTISTS CAMBRIDGE UNIVERSITY PRESS (2022).

3.HAYDEN LIU, PYTHON MACHINE LEARNING BY EXAMPLE – PACKT (2022) – THIRD EDITION.

4.JOHN V. GUTTAG, INTRODUCTION TO COMPUTATION AND PROGRAMMING USING PYTHON WITH APPLICATION TO COMPUTATIONAL MODELING AND UNDERSTANDING DATA – MIT PRESS (2021) – THIRD EDITION.

	More Information
	COURSE ATTENDANCE IS STRONGLY RECOMMENDED. STUDENTS MUST BE PREPARED TO SPEND A FAIR AMOUNT OF TIME IN THE STUDY OUTSIDE OF LESSONS. FOR A SATISFACTORY PREPARATION STUDENTS NEED TO SPEND AN AVERAGE OF ONE HOUR OF STUDY TIME FOR EACH HOUR SPENT IN CLASS, AND ABOUT 80 HOURS FOR DEVELOPING THE PROJECT. COURSE MATERIALS WILL BE AVAILABLE FOR DOWNLOAD FROM THE DEPARTMENTAL E-LEARNING PLATFORM HTTP://ELEARNING.INFORMATICA.UNISA.IT/EL-PLATFORM/ CONTACTS PROF. GIUSEPPE POLESE GPOLESE@UNISA.IT PROF. LOREDANA CARUCCIO LCARUCCIO@UNISA.IT

More Information

COURSE ATTENDANCE IS STRONGLY RECOMMENDED. STUDENTS MUST BE PREPARED TO SPEND A FAIR AMOUNT OF TIME IN THE STUDY OUTSIDE OF LESSONS. FOR A SATISFACTORY PREPARATION STUDENTS NEED TO SPEND AN AVERAGE OF ONE HOUR OF STUDY TIME FOR EACH HOUR SPENT IN CLASS, AND ABOUT 80 HOURS FOR DEVELOPING THE PROJECT.

COURSE MATERIALS WILL BE AVAILABLE FOR DOWNLOAD FROM THE DEPARTMENTAL
E-LEARNING PLATFORM HTTP://ELEARNING.INFORMATICA.UNISA.IT/EL-PLATFORM/

CONTACTS
PROF. GIUSEPPE POLESE
GPOLESE@UNISA.IT
PROF. LOREDANA CARUCCIO
LCARUCCIO@UNISA.IT

Lessons Timetable

BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2024-12-13]

LOREDANA CARUCCIO | MACHINE LEARNING