CIRIACO D'AMBROSIO | DECISION SUPPORT SYSTEMS

cod. 0522200049

DECISION SUPPORT SYSTEMS

	0522200049
	DEPARTMENT OF MATHEMATICS
	EQF7
	MATHEMATICS
	2023/2024

CURRICULUM APPLICATION-ORIENTED MATHEMATICS

	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2018
	SPRING SEMESTER

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
	MAT/09	6	48	LESSONS	SUPPLEMENTARY COMPULSORY SUBJECTS

PROFESSORS

	RAFFAELE CERULLI T Curriculum
	CIRIACO D'AMBROSIO Curriculum

	Objectives
	KNOWLEDGE AND UNDERSTANDING: THE COURSE AIMS TO PROVIDE A SERIES OF TOOLS TO ADDRESS DECISION-MAKING PROBLEMS CHARACTERIZED BY A HIGH LEVEL OF COMPLEXITY, BY SITUATIONS OF DATA UNCERTAINTY, OR BY THE PRESENCE OF MULTIPLE AND CONFLICTUAL OBJECTIVES. APPROACHES BASED ON MATHEMATICAL MODELS FOR AUTOMATIC LEARNING (MACHINE LEARNING) AND DATA MINING: CLASSIFICATION AND IDENTIFICATION PROBLEMS. MINIMIZATION PROBLEMS OF CLASSIFICATION ERRORS. ABILITY TO APPLY ACQUIRED KNOWLEDGE AND UNDERSTANDING: THE COURSE HAS A STRONG METHODOLOGICAL AND APPLICATION CONNOTATION. IN ADDITION TO THE NECESSARY THEORETICAL CONTENT, PARTICULAR EMPHASIS WILL BE GIVEN TO THE USE OF SOFTWARE TOOLS AND SPECIAL ATTENTION WILL BE DEVOTED TO THE DEVELOPMENT OF EXAMPLES AND PROJECTS BY STUDENTS.

KNOWLEDGE AND UNDERSTANDING:
THE COURSE AIMS TO PROVIDE A SERIES OF TOOLS TO ADDRESS DECISION-MAKING PROBLEMS CHARACTERIZED BY A HIGH LEVEL OF COMPLEXITY, BY SITUATIONS OF DATA UNCERTAINTY, OR BY THE PRESENCE OF MULTIPLE AND CONFLICTUAL OBJECTIVES. APPROACHES BASED ON MATHEMATICAL MODELS FOR AUTOMATIC LEARNING (MACHINE LEARNING) AND DATA MINING: CLASSIFICATION AND IDENTIFICATION PROBLEMS. MINIMIZATION PROBLEMS OF CLASSIFICATION ERRORS.
ABILITY TO APPLY ACQUIRED KNOWLEDGE AND UNDERSTANDING:
THE COURSE HAS A STRONG METHODOLOGICAL AND APPLICATION CONNOTATION. IN ADDITION TO THE NECESSARY THEORETICAL CONTENT, PARTICULAR EMPHASIS WILL BE GIVEN TO THE USE OF SOFTWARE TOOLS AND SPECIAL ATTENTION WILL BE DEVOTED TO THE DEVELOPMENT OF EXAMPLES AND PROJECTS BY STUDENTS.

	Prerequisites
	STUDENTS SHOULD KNOW BASIC CONCEPTS OF DISCRETE MATHEMATICS AND OPERATION RESEARCH AND OF PROBABILITY THEORY AND STATISTICS

	Contents
	THE COURSE PROVIDES CONTENTS OF TYPE SCIENTIFIC AND TECHNOLOGICAL. SCIENTIFIC CONTRIBUTIONS ARE RELATE TO KNOWLEDGE TO DEVELOP A DATA ANALYTICS MODULE AND CONCERN OPTIMIZATION SKILLS APPLIED TO THE MODELING OF DECISION MAKING PROCESSES. IN PARTICULAR THE FOLLOWING TOPICS WILL BE ADDRESSED: - FORECAST MODELS: NEURAL NETWORKS (FEEDFORWARD, CONVOLUTIONAL, DEEP LEARNING) - SUPERVISED LEARNING: MACHINE LEARNING MODELS: THE PERCEIVER, ADALINE, SUPPORT VECTOR MACHINE - UNSUPERVISED LEARNING: CLUSTER TECHNIQUES. K-MEANS ALGORITHM USE OF INFORMATION TOOLS (HARDWARE AND SOFTWARE) FOR THE IMPLEMENTATION OF SIMPLE DECISION-MAKING MODELS. BASIC PRINCIPLES OF THE PYTHON LANGUAGE .

Contents

THE COURSE PROVIDES CONTENTS OF TYPE SCIENTIFIC AND TECHNOLOGICAL. SCIENTIFIC CONTRIBUTIONS ARE RELATE TO KNOWLEDGE TO DEVELOP A DATA ANALYTICS MODULE AND CONCERN OPTIMIZATION SKILLS APPLIED TO THE MODELING OF DECISION MAKING PROCESSES. IN PARTICULAR THE FOLLOWING TOPICS WILL BE ADDRESSED:
- FORECAST MODELS: NEURAL NETWORKS (FEEDFORWARD, CONVOLUTIONAL, DEEP LEARNING)
- SUPERVISED LEARNING: MACHINE LEARNING MODELS: THE PERCEIVER, ADALINE, SUPPORT VECTOR MACHINE
- UNSUPERVISED LEARNING: CLUSTER TECHNIQUES. K-MEANS ALGORITHM
USE OF INFORMATION TOOLS (HARDWARE AND SOFTWARE) FOR THE IMPLEMENTATION OF SIMPLE DECISION-MAKING MODELS. BASIC PRINCIPLES OF THE PYTHON LANGUAGE .

	Teaching Methods
	THE COURSE IS ORGANIZED IN 48 HOURS OF FRONTAL LESSONS (6 CFU), USING PROJECTED SLIDES. AT THE END OF EACH TOPIC, SOME APPLICATION EXAMPLES AND EXERCISES ARE PRESENTED.

	Verification of learning
	THE FINAL EXAM IS DESIGNED TO EVALUATE AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, AS WELL AS THE ABILITY TO APPLY SUCH KNOWLEDGE FOR THE RESOLUTION OF DECISION PROBLEMS. THE ORAL EXAMINATION WILL COVER ALL THE TOPICS OF THE COURSE AND THE ASSESSMENT WILL TAKE INTO ACCOUNT THE KNOWLEDGE DEMONSTRATED BY THE STUDENT CONCERNING BOTH THE THEORETICAL AND APPLICATIVE ASPECTS FOR THE RESOLUTION OF DECISION PROBLEMS. THE EVALUATION OF ORAL, EXPRESSED IN THIRTIES, TAKES INTO ACCOUNT THE ABILITY TO DESCRIBE THE ALGORITHMS AND THE OTHER CONCEPTS, PRESENTED IN THE COURSE, IN A CLEAR AND CONCISE MANNER. THE MINIMUM ASSESSMENT LEVEL (18) IS AWARDED WHEN THE STUDENT SHOWS A FRAGMENTARY KNOWLEDGE OF THEORETICAL CONTENTS AND A LIMITED ABILITY TO FORMULATE DECISION PROBLEMS AND TO APPLY ALGORITHMS TO SOLVE THEM. THE MAXIMUM ASSESSMENT LEVEL (30) IS ATTRIBUTED WHEN THE STUDENT SHOWS A COMPLETE AND IN-DEPTH KNOWLEDGE OF THE COURSE TOPICS AND A REMARKABLE ABILITY TO IDENTIFY THE MOST APPROPRIATE METHODS TO SOLVE DECISION PROBLEMS FACED. THE LAUDE IS ATTRIBUTED WHEN THE CANDIDATE SHOWS A SIGNIFICANT MASTERY OF THE THEORETICAL AND OPERATIONAL CONTENTS AND SHOWS THE ABILITY TO PRESENT THE TOPICS WITH REMARKABLE PROPERTIES OF LANGUAGE AND AUTONOMOUS PROCESSING CAPACITY EVEN IN CONTEXTS DIFFERENT FROM THOSE PROPOSED BY THE TEACHER.

Verification of learning

THE FINAL EXAM IS DESIGNED TO EVALUATE AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, AS WELL AS THE ABILITY TO APPLY SUCH KNOWLEDGE FOR THE RESOLUTION OF DECISION PROBLEMS.
THE ORAL EXAMINATION WILL COVER ALL THE TOPICS OF THE COURSE AND THE ASSESSMENT WILL TAKE INTO ACCOUNT THE KNOWLEDGE DEMONSTRATED BY THE STUDENT CONCERNING BOTH THE THEORETICAL AND APPLICATIVE ASPECTS FOR THE RESOLUTION OF DECISION PROBLEMS.

THE EVALUATION OF ORAL, EXPRESSED IN THIRTIES, TAKES INTO ACCOUNT THE ABILITY TO DESCRIBE THE ALGORITHMS AND THE OTHER CONCEPTS, PRESENTED IN THE COURSE, IN A CLEAR AND CONCISE MANNER.

THE MINIMUM ASSESSMENT LEVEL (18) IS AWARDED WHEN THE STUDENT SHOWS A FRAGMENTARY KNOWLEDGE OF THEORETICAL CONTENTS AND A LIMITED ABILITY TO FORMULATE DECISION PROBLEMS AND TO APPLY ALGORITHMS TO SOLVE THEM.

THE MAXIMUM ASSESSMENT LEVEL (30) IS ATTRIBUTED WHEN THE STUDENT SHOWS A COMPLETE AND IN-DEPTH KNOWLEDGE OF THE COURSE TOPICS AND A REMARKABLE ABILITY TO IDENTIFY THE MOST APPROPRIATE METHODS TO SOLVE DECISION PROBLEMS FACED.

THE LAUDE IS ATTRIBUTED WHEN THE CANDIDATE SHOWS A SIGNIFICANT MASTERY OF THE THEORETICAL AND OPERATIONAL CONTENTS AND SHOWS THE ABILITY TO PRESENT THE TOPICS WITH REMARKABLE PROPERTIES OF LANGUAGE AND AUTONOMOUS PROCESSING CAPACITY EVEN IN CONTEXTS DIFFERENT FROM THOSE PROPOSED BY THE TEACHER.

	Texts
	- SEBASTIAN RASCHKA, VAHID MIRJALILI: PYTHON MACHINE LEARNING - THIRD EDITION, 2019. - AURÉLIEN GÉRON: HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN KERAS AND TENSORFLOW 2ND EDITION, O'REILLY, 2019. - HORSTMANN CAY, RANCE D. NECAISE: PYTHON FOR EVERYONE, 3/E, 2018, WILEY. - GEORGE L. NEMHAUSER, LAURENCE A. WOLSEY, INTEGER AND COMBINATORIAL OPTIMIZATION, 1999. - DIMITRIS BERTSIMAS, JACK DUNN, MACHINE LEARNING UNDER A MODERN OPTIMIZATION LENS, DYNAMIC IDEAS LLC, 2019 - LECTURE NOTES PROVIDED BY THE TEACHER.

Texts

- SEBASTIAN RASCHKA, VAHID MIRJALILI: PYTHON MACHINE LEARNING - THIRD EDITION, 2019.
- AURÉLIEN GÉRON: HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN KERAS AND TENSORFLOW 2ND EDITION, O'REILLY, 2019.
- HORSTMANN CAY, RANCE D. NECAISE: PYTHON FOR EVERYONE, 3/E, 2018, WILEY.
- GEORGE L. NEMHAUSER, LAURENCE A. WOLSEY, INTEGER AND COMBINATORIAL OPTIMIZATION, 1999.
- DIMITRIS BERTSIMAS, JACK DUNN, MACHINE LEARNING UNDER A MODERN OPTIMIZATION LENS, DYNAMIC IDEAS LLC, 2019
- LECTURE NOTES PROVIDED BY THE TEACHER.

	More Information
	- THE COURSE LANGUAGE IS ITALIAN. - PARTICIPATION TO BOTH LECTURES AND EXERCISE - SESSIONS IS STRONGLY RECOMMENDED.- OFFICE - HOURS FOR STUDENTS ARE AVAILABLE ON THE WEBPAGE: HTTPS://DOCENTI.UNISA.IT/001227/HOME

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CIRIACO D'AMBROSIO | DECISION SUPPORT SYSTEMS