Teaching groups languages
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Prerequisites
There are no specific prerequisites. Students must have mathematical skills at a graduate level of a scientific or technological degree.
Objectives and Contextualisation
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.
Competences
 Analyse complex systems in different fields and determine the basic structures and parameters of their workings.
 Analyse, synthesise, organise and plan projects in the field of study.
 Apply specific methodologies, techniques and resources to conduct research and produce innovative results in the area of specialisation.
 Apply techniques for solving mathematical models and their real implementation problems.
 Communicate and justify conclusions clearly and unambiguously to both specialised and nonspecialised audiences.
 Formulate, analyse and validate mathematical models of practical problems in different fields.
 Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
 Isolate the main difficulty in a complex problem from other, less important issues.
 Look for new areas to open up within the field.
 Recognise the human, economic, legal and ethical dimension in professional practice.
 Solve complex problems by applying the knowledge acquired to areas that are different to the original ones.
 Solve problems in new or littleknown situations within broader (or multidisciplinary) contexts related to the field of study.
Learning Outcomes
 Analyse, synthesise, organise and plan projects in the field of study.
 Apply specific methodologies, techniques and resources to conduct research and produce innovative results in the area of specialisation.
 Check the validity of the model with regard to the behaviour of the real system.
 Communicate and justify conclusions clearly and unambiguously to both specialised and nonspecialised audiences.
 Describe the functional dependencies of the system with regard to the different parameters
 Design mathematical models that represent the system and its behaviour.
 Identify the parameters that determine how a system works.
 Implement the proposed solutions reliably and efficiently.
 Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
 Isolate the main difficulty in a complex problem from other, less important issues.
 Look for new areas to open up within the field.
 Recognise the human, economic, legal and ethical dimension in professional practice.
 Solve complex problems by applying the knowledge acquired to areas that are different to the original ones.
 Solve mathematical models efficiently.
 Solve problems in new or littleknown situations within broader (or multidisciplinary) contexts related to the field of study.
Content
We have two types of activities during the semester: to attend a three innovative minicourses and attend a series of lectures given by people who work for companies or researchers working in universities or research centres.
The courses are the following:
 Modeling in the cloud. Introduction to asset impact, cat risk and early warning. How to model natural hazards. From the model to a cloud service.
 Introduction to Python for analytical purposes. Python basics. Data with Python. Problem solving with Python. Machine Learning with Python.
 Machine learning. Machine learning, artificial intelligence and data science: from deterministic to stochastic point of view. Supervised and unsupervised techniques: from trees to random forests. Introduction to neural networks and mathematical challenges: performance assessment. ROC curves and cross validation.
We will invite specialists in the fields of Modelling Complex Systems, Modelling of Engineering, Mathematical Modelling and Data Science. Among the others we will have talks from people coming from:

 IIIA, Institut d'Intel·ligència Artificial, https://www.iiia.csic.es
 CRM, Centre de Recerca Matemàtica, http://www.crm.cat
 Accenture, https://www.accenture.com
 DSBlab, Dynamical Systems Biology lab (UPF), https://www.upf.edu/web/dsb
 Meteosim, https://www.meteosim.com
Activities and Methodology
Title 
Hours 
ECTS 
Learning Outcomes 
Type: Directed 



Attending Lectures 
16

0.64 
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

Attending Minicourses 
22

0.88 
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

The methodology of the three courses is based on master classes which consist in the presentation of the theory, examples and some case studies.
Relating the lectures, they will be announced previously on the virtual campus of the module Research and Innovation. There the students will find the title of the talk, the name of the speaker, a short summary and links of interest.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
Title 
Weighting 
Hours 
ECTS 
Learning Outcomes 
Attending Lectures 
10% 
16

0.64 
3, 4, 5, 7, 9, 15

Making a report on Maching Learning 
30% 
32

1.28 
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

Making a report on Natural Hazards 
30% 
32

1.28 
3, 4, 5, 7, 9, 15

Report on Python for analitical purposes 
30% 
32

1.28 
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

Students must present three projects corresponding to the three courses taught, in groups of two or three people.
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/pythonfordatascience
 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 9780387310732. 2006.
 Ethem Alpaydin. Introduction to Machine Learning (Fourth ed.). MIT. 2020.
Software
The software will be detailed in each one of the courses.
Language list
Name 
Group 
Language 
Semester 
Turn 
(TEm) Theory (master) 
1 
English 
first semester 
afternoon 