Raffaele CERULLI | DECISION SUPPORT SYSTEMS

cod. 0522200049

DECISION SUPPORT SYSTEMS

	0522200049
	DIPARTIMENTO DI MATEMATICA
	EQF7
	MATHEMATICS
	2020/2021

CURRICULUM APPLICATION-ORIENTED MATHEMATICS

	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2018
	SECONDO SEMESTRE

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 OF DECISION SUPPORTING SYSTEMS PROPOSES TO PROVIDE ANALYTICAL SKILLS FOR ADDRESSING GENERAL DECISION-MAKING ISSUES • STUDY THE DECISION-MAKING PROBLEM THROUGH THE STUDY OF DECISION-MAKING MODELS, METHODS THAT USE THESE MODELS, EXAMPLES AND APPLICATION CASES. THE MODELS CONSIDERED ARE OF THE QUANTITATIVE TYPE. THE GENERAL OBJECT OF THE COURSE IS THEREFORE THE DECISION MAKING PROCESS AND THE OBJECTIVE IS TO PROVIDE FORMAL TOOLS WITH WHICH SUPPORT IT. YOU WILL ACQUIRE BASIC KNOWLEDGE FOR THE DESIGN OF EFFICIENT TOOLS FOR DATA ANALYSIS AND DATA MINING.

KNOWLEDGE AND UNDERSTANDING:
THE COURSE OF DECISION SUPPORTING SYSTEMS PROPOSES TO PROVIDE ANALYTICAL SKILLS FOR ADDRESSING GENERAL DECISION-MAKING ISSUES • STUDY THE DECISION-MAKING PROBLEM THROUGH THE STUDY OF DECISION-MAKING MODELS, METHODS THAT USE THESE MODELS, EXAMPLES AND APPLICATION CASES. THE MODELS CONSIDERED ARE OF THE QUANTITATIVE TYPE. THE GENERAL OBJECT OF THE COURSE IS THEREFORE THE DECISION MAKING PROCESS AND THE OBJECTIVE IS TO PROVIDE FORMAL TOOLS WITH WHICH SUPPORT IT.
YOU WILL ACQUIRE BASIC KNOWLEDGE FOR THE DESIGN OF EFFICIENT TOOLS FOR DATA ANALYSIS AND DATA MINING.

	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) - MODELS OF FULL PROGRAMMING AND STURDY OPTIMIZATION - METALIZATION RESOLUTIVE TECHNIQUES: ITERATED LOCAL SEARCH, VARIABLE NEIGHBORHOOD SEARCH, ITERATED GREEDY. USE OF INFORMATION TOOLS (HARDWARE AND SOFTWARE) FOR THE IMPLEMENTATION OF SIMPLE DECISION-MAKING MODELS.

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)
- MODELS OF FULL PROGRAMMING AND STURDY OPTIMIZATION
- METALIZATION RESOLUTIVE TECHNIQUES: ITERATED LOCAL SEARCH, VARIABLE NEIGHBORHOOD SEARCH, ITERATED GREEDY.
USE OF INFORMATION TOOLS (HARDWARE AND SOFTWARE) FOR THE IMPLEMENTATION OF SIMPLE DECISION-MAKING MODELS.

	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 SOLUTION OF DECISIONAL PROBLEM. THE EXAM CONSISTS OF A WRITTEN TEST AND AN ORAL INTERVIEW. THE WRITTEN TEST CONSISTS OF SOLVING TYPICAL PROBLEMS PRESENTED IN THE COURSE AND ANSWERING QUESTIONS RELATED TO TOPICS OF THE COURSE. USUALLY, THE WRITTEN TEST LASTS 120 MINUTES. THE ORAL EXAMINATION WILL COVER ALL THE TOPICS OF THE COURSE AND ASSESSMENT WILL TAKE INTO ACCOUNT THE KNOWLEDGE DEMONSTRATED BY THE STUDENT IN THE MODELING AND RESOLUTION OF LINEAR PROGRAMMING PROBLEMS. THE EVALUATION OF THE WRITTEN TEST AND OF THE ORAL EXAMINATION IS EXPRESSED IN THIRTIES. IT IS NECESSARY TO GAIN AT LEAST 18/30 IN BOTH TESTS TO PASS THE EXAM. TO STIMULATE THE ATTENDANCE OF THE COURSE, IT IS GIVEN TO THE STUDENT THE CHANCE TO CONFIRM THE WRITTEN EXAMINATION SCORE IN THE FIRST EXAMINATION SESSION AFTER THE COURSE.

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 SOLUTION OF DECISIONAL PROBLEM.

THE EXAM CONSISTS OF A WRITTEN TEST AND AN ORAL INTERVIEW. THE WRITTEN TEST CONSISTS OF SOLVING TYPICAL PROBLEMS PRESENTED IN THE COURSE AND ANSWERING QUESTIONS RELATED TO TOPICS OF THE COURSE. USUALLY, THE WRITTEN TEST LASTS 120 MINUTES. THE ORAL EXAMINATION WILL COVER ALL THE TOPICS OF THE COURSE AND ASSESSMENT WILL TAKE INTO ACCOUNT THE KNOWLEDGE DEMONSTRATED BY THE STUDENT IN THE MODELING AND RESOLUTION OF LINEAR PROGRAMMING PROBLEMS. THE EVALUATION OF THE WRITTEN TEST AND OF THE ORAL EXAMINATION IS EXPRESSED IN THIRTIES. IT IS NECESSARY TO GAIN AT LEAST 18/30 IN BOTH TESTS TO PASS THE EXAM. TO STIMULATE THE ATTENDANCE OF THE COURSE, IT IS GIVEN TO THE STUDENT THE CHANCE TO CONFIRM THE WRITTEN EXAMINATION SCORE IN THE FIRST EXAMINATION SESSION AFTER THE COURSE.

	Texts
	LECTURE NOTES

	More Information
	- THE COURSE LANGUAGE IS ITALIAN. - PARTICIPATION TO BOTH LECTURES AND EXERCISE SESSIONS IS STRONGLY RECOMMENDED.

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Raffaele CERULLI | DECISION SUPPORT SYSTEMS