Algorithms in Fintech

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.

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 convex nonlinear programming problems.

The course has no specific prerequisites apart from basic math knowledge

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)

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.

Attendence is strongly recommended, but not mandatory.

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 Python fundamental aspects and tools.

Final written exam followed by a brief discussion of students’ scripts. It consists of:

- written questions and exercises (on theoretical aspects)
- exercises to be solved, also relying on Python.

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.

No fixed percentage weighting is assigned to individual assessment components in the calculation of the final grade. As specified above, ongoing assessment is based on a holistic evaluation of the student's overall participation, engagement, and performance throughout the course.