Specific goals are detailed next according to Dublin descriptors.

- Knowledge and understanding: at the end of the course, students are expected to know the fundamental aspects of mathematical programming theory and algorithms as support to decision making in finance and data analysis.

- Applying knowledge and understanding: at the end of the course, students are expected to know how to rely on mathematical programming tools and methods, and computer software to practically address real-world problems in finance and data analysis.

- Making judgements: the whole course is organized so as to make the students ask (themselves) the “right” questions. To achieve this objective, computer lab activities, exercise sessions, homework assignments, case study analyses are resorted to in a flipped classroom context.

- Communication: students are continuously invited to lead lectures and participate directly and actively in the learning process in flipped classroom schemes.

- Lifelong learning skills: lectures are devised to encourage self-motivated pursuit of knowledge. In fact, as detailed above, but also in the light of an ongoing evaluation approach, students are urged to develop a leading role during the lectures in a cooperative, as well as competitive environment.

Curriculum

Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.Mutuazione: 21210159 ALGORITHMS IN FINTECH in Finanza e impresa LM-16 LAMPARIELLO LORENZO

Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.Mutuazione: 21210159 ALGORITHMS IN FINTECH in Finanza e impresa LM-16 LAMPARIELLO LORENZO

Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.Mutuazione: 21210159 ALGORITHMS IN FINTECH in Finanza e impresa LM-16 LAMPARIELLO LORENZO

Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.Mutuazione: 21210159 ALGORITHMS IN FINTECH in Finanza e impresa LM-16 LAMPARIELLO LORENZO

Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.Mutuazione: 21210159 ALGORITHMS IN FINTECH in Finanza e impresa LM-16 LAMPARIELLO LORENZO

Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.Mutuazione: 21210159 ALGORITHMS IN FINTECH in Finanza e impresa LM-16 LAMPARIELLO LORENZO

Programma

The course focuses on the fundamental aspects of mathematical programming theory and algorithms. Main topics are organized according to the following learning units.Unit 1 - Applied Aspects (30 hours)

1.a (15 hours) Modeling techniques through mathematical programming in the context of financial problems and data analysis.

1.b (15 hours) How to practically solve problems’ models (via Python).

Unit 2 - Theory (30 hours)

2.a Theoretical properties concerning linear and (convex) nonlinear programming problems.

2.b Main algorithms for linear and nonlinear programming problems.

Testi Adottati

Brinkhuis J., Tikhomirov V. (2005) Optimization: insights and applications (Princeton University Press)Hillier F.S., Lieberman G.J. (2015) Introduction to Operations Research (McGraw-Hill Education)

Cherkassky V., Mulier F.M. (2007) Learning from data: concepts, theory, and methods (John Wiley & Sons)

Cornuejols G., Tütüncü R. (2006) Optimization methods in finance (Cambridge University Press)

Modalità Erogazione

By virtue of its very applied nature, the course is organized in: - computer lab activities; - exercise sessions; - case study analyses. Within a flipped classroom framework, students are encouraged to participate directly and actively in the learning process in a cooperative, as well as competitive environment. The active attitude throughout the lessons is in turn instrumental for the students to better internalize and independently elaborate the underlying elementary meaning that is behind the main mathematical concepts.Modalità Valutazione

Ongoing evaluation approach: to stimulate an active attitude throughout the course, the students’ behaviour during the classes and willingness to participate in learning activities are taken into account in the evaluation process: more specifically, students who make themselves noteworthy in a positive way (by, e.g., a proactive participation or an excellent effort evident in office hours) are rewarded with positive marks that will contribute to the final grade. Exams: computer-based mid-term exam: to ascertain the comprehension of basic modeling techniques, and the understanding of computer softwares fundamental tools. Final written exam followed by a brief discussion of students’ scripts. It consists of: - written questions (on theoretical aspects); - exercises to be solved. The aim is to verify how the students are able to elaborate independently the main topics of the course, and to rely on mathematical programming techniques and computer software to practically deal with real-world problems in finance and data analysis.