Difference between revisions of "Models matemàtics de la tecnologia"
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(Created page with "== Rooms == * 8:00–10:00: room 002. * 10:30–12:30: rooms 002, 004 and PC2. == Important dates == * For the lab part, we will have midterm presentations Nov 9th. * For the...") |
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* For the lab part, we will have midterm presentations Nov 9th. | * For the lab part, we will have midterm presentations Nov 9th. | ||
* For the lab part, we will have the final presentations Dec 21th (or in January?). | * For the lab part, we will have the final presentations Dec 21th (or in January?). | ||
* Nov | * Nov 18th: first 4 modules of the online course on information management should be complete by then, and the research by topic should also be complete. | ||
== Units == | == Units == | ||
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#* Chapters 1 to 8 of the book Sam Howison: ''Practical Applied Mathematics: Modelling, Analysis, Approximation''. Cmabridge Unuiversity Press, 2005. | #* Chapters 1 to 8 of the book Sam Howison: ''Practical Applied Mathematics: Modelling, Analysis, Approximation''. Cmabridge Unuiversity Press, 2005. | ||
#* Presentations will be based on a chapter (or part) of this book. | #* Presentations will be based on a chapter (or part) of this book. | ||
# Modelling laboratory (main part of the course) | |||
== Grade == | == Grade == | ||
* Information unit: 10% | * Information unit: 10% (groups of 3, you will do the groups) | ||
* Student seminar + participation in seminar: 25% | * Student seminar + participation in seminar: 25% (groups of 3 different from IU) | ||
* Project: 40% | * Project: 40% (groups of 4-5 according to interest) | ||
* Optional (exam): 25% | * Optional (exam): 25% | ||
== Projects == | |||
=== Corporate Default Forecasting with Machine Learning === | |||
''Jordi Moragas, Bluecap Management Consulting'' | |||
Machine learning to predict the probability that someone will be able to get into a loan without faulting. We will focus on the data science process. Python. | |||
=== COVID Vaccine Roll-out solution === | |||
''Sandra O., Patricio P., Tomás B., Quim A.; Accenture'' | |||
Python, but there's another possibility. | |||
=== '''Learning''' to throw rigid objects with a robotic arm === | |||
''Adrià Colomé, IRII'' | |||
We will teach a robot to throw an object from a starting point to a target one. Python (or Matlab). | |||
=== Motion planning for a car-like robot in a cluttered environment === | |||
''Federico Thomas, IRI'' | |||
We'll determine a path between a start and end point on a plane with obstacles. The car will have to maneuver. Matlab. | |||
=== Improving techniques for carbon capture (and other environmental contaminants) === | |||
''Tim Myers, Centre de Recerca Matemàtica'' | |||
Mathematical model which measures how much CO2 is adsorbed by some weird thingy they've made. | |||
=== Coral reefs. Their importance and demise === | |||
''Dr Lucy C Auton, CRM'' | |||
Model to evaluate the key actions we can take to partially fix the problem. Matlab. | |||
Latest revision as of 10:04, 14 September 2022
Rooms
- 8:00–10:00: room 002.
- 10:30–12:30: rooms 002, 004 and PC2.
Important dates
- For the lab part, we will have midterm presentations Nov 9th.
- For the lab part, we will have the final presentations Dec 21th (or in January?).
- Nov 18th: first 4 modules of the online course on information management should be complete by then, and the research by topic should also be complete.
Units
- Information management (online)
- Seminars: Applied Mathematics Seminar and Professional/Entrepreneurship Seminar
- Students seminar
- Chapters 1 to 8 of the book Sam Howison: Practical Applied Mathematics: Modelling, Analysis, Approximation. Cmabridge Unuiversity Press, 2005.
- Presentations will be based on a chapter (or part) of this book.
- Modelling laboratory (main part of the course)
Grade
- Information unit: 10% (groups of 3, you will do the groups)
- Student seminar + participation in seminar: 25% (groups of 3 different from IU)
- Project: 40% (groups of 4-5 according to interest)
- Optional (exam): 25%
Projects
Corporate Default Forecasting with Machine Learning
Jordi Moragas, Bluecap Management Consulting
Machine learning to predict the probability that someone will be able to get into a loan without faulting. We will focus on the data science process. Python.
COVID Vaccine Roll-out solution
Sandra O., Patricio P., Tomás B., Quim A.; Accenture
Python, but there's another possibility.
Learning to throw rigid objects with a robotic arm
Adrià Colomé, IRII
We will teach a robot to throw an object from a starting point to a target one. Python (or Matlab).
Motion planning for a car-like robot in a cluttered environment
Federico Thomas, IRI
We'll determine a path between a start and end point on a plane with obstacles. The car will have to maneuver. Matlab.
Improving techniques for carbon capture (and other environmental contaminants)
Tim Myers, Centre de Recerca Matemàtica
Mathematical model which measures how much CO2 is adsorbed by some weird thingy they've made.
Coral reefs. Their importance and demise
Dr Lucy C Auton, CRM
Model to evaluate the key actions we can take to partially fix the problem. Matlab.
