Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Research and Innovation
Code: 43475Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Modelling for Science and Engineering | OB | 1 |
Contact lecturer
- Name :
- Silvia Cuadrado Gavilan
- Email :
- silvia.cuadrado@uab.cat
Teaching staff
- Ana Cortes Fite
- Carles Carrillo Jordan
- Isabel Serra Mochales
- Víctor Navas Portella
Group languages
You can consult this information at the end of the document.
Prerequisites
There are no specific prerequisites. Students must have mathematical skills at a graduate level of a scientific or technological degree.
Objectives
The aim of this module is to show students the variety of fields in which they will be able to apply the tools acquired during the Master courses. Hopefully they will be able to use them as guidance when looking for Internships in Companies and Institutions and also when choosing a topic and an advisor for the Master’s Thesis. We also expect that it will help them to find a career path.
Learning outcomes
- CA06 (Properly integrate modelling tools and/or results from different fields or of different types, especially in the context of multidisciplinary work environments.) Properly integrate modelling tools and/or results from different fields or of different types, especially in the context of multidisciplinary work environments.
- CA07 (Critically assess the need for and presence of ethical, sustainability, gender equality and/or social justice criteria in studies or modelling projects.) Critically assess the need for and presence of ethical, sustainability, gender equality and/or social justice criteria in studies or modelling projects.
- CA08 (Work in multidisciplinary teams on the development of activities and projects in the field of Modelling.) Work in multidisciplinary teams on the development of activities and projects in the field of Modelling.
- KA06 (Identify the programming languages and environments, as well as the most relevant mathematical tools, that are used in the industrial and research field.) Identify the programming languages and environments, as well as the most relevant mathematical tools, that are used in the industrial and research field.
- KA07 (Identify the main sectors and professional contexts in which mathematical modelling is applied.) Identify the main sectors and professional contexts in which mathematical modelling is applied.
- KA08 (Describe the main results and/or predictions of the different mathematical tools used in the professional sector to construct models.) Describe the main results and/or predictions of the different mathematical tools used in the professional sector to construct models.
- SA07 (Correctly contextualise computer applications aimed at modelling an industrial or specific process, using an appropriate programming environment.) Correctly contextualise computer applications aimed at modelling an industrial or specific process, using an appropriate programming environment.
- SA08 (Properly interpret the results and predictions obtained from applying a mathematical model to solve specific problems in the industrial or research field.) Properly interpret the results and predictions obtained from applying a mathematical model to solve specific problems in the industrial or research field.
Contents
During the semester, there will be two types of activities: three short courses and a series of seminars delivered by professionals working in industry as well as researchers from universities and research centers.
The courses are as follows:
Cloud-Based Modeling. Catastrophic risks and early warning systems. How to model natural hazards. From a mathematical model to a cloud-based service.
Python for Data Analytics. Basic concepts of Python. Data handling with Python. Problem solving using Python. Machine learning with Python.
Machine Learning. Machine learning, artificial intelligence, and data science: from deterministic to stochastic approaches. Supervised and unsupervised learning techniques: from decision trees to random forests. Introduction to neural networks and their mathematical challenges: performance evaluation. ROC curves and cross-validation.
For the seminar series, we will invite experts in the fields of Complex Systems Modeling, Engineering Modeling, Mathematical Modeling, and Data Science.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Attending Mini-courses | 22 | 0.88 | CA06, CA08, KA06, KA07, KA08, SA07, SA08 |
| Attending Lectures | 16 | 0.64 | CA06, CA07, CA08, KA07, SA08 |
The methodology of the three courses is based on lectures consisting of the presentation of theoretical concepts, illustrative examples, and selected practical case studies.
Regarding the seminars, they will be announced in advance on the virtual campus of the Research and Innovation module. Students will find the seminar title, the speaker's name, a brief abstract, and relevant links for further information.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Report on Python for analitical purposes | 30% | 32 | 1.28 | CA06, CA08, KA06, KA08, SA07, SA08 |
| Making a report on Maching Learning | 30% | 32 | 1.28 | CA06, CA08, KA06, KA08, SA07, SA08 |
| Making a report on Natural Hazards | 30% | 32 | 1.28 | CA06, CA08, KA06, KA07, KA08, SA07, SA08 |
| Attending Lectures | 10% | 16 | 0.64 | CA06, CA07, KA07, KA08, SA08 |
Students must present three projects corresponding to the three courses taught.
Each of these projects accounts for 30% of the grade.
On the other hand, attendance to the talks, which is mandatory, contributes 10% of the final grade.
Bibliography
- Bibliography and links of interest
- https://www.python.org/about/gettingstarted/
- https://www.learnpython.org/
- https://learntocodewith.me/posts/python-for-data-science
- Pitts W McCulloch W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 1943.
- L. Breiman, J.H. Friedman, R.A. Olshen and C.J Stone. Classification and Regression Trees. Wadsworth, Belmont, Ca, 1988.
- Friedman, Jerome H. Data Mining and Statistics: What's the connection?\". Computing Science and Statistics. 29. 1998.
- B Ripley. Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge. 2002.
- T Hastie, R Tibshirani, J Friedman. The Elements of Statistical Learning. Data Mining, Inference and Prediction, Springer, New York. 2009.
- Bishop, C. M. Pattern Recognition and Machine Learning, Springer, ISBN 978-0-387-31073-2. 2006.
- Ethem Alpaydin. Introduction to Machine Learning (Fourth ed.). MIT. 2020.
Software
The software will be detailed in each one of the courses.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TEm) Theory (master) | 1 | English | first semester | afternoon |