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Data Analysis, Optimization and Decision Making

Code: 44733
Credits: 6
2026/2027
Degree programme Type Course
Research and Innovation in Computer based Science and Engineering OP 1

Contact lecturer

Name :
Carles Pedret Ferre
Email :
carles.pedret@uab.cat

Teaching staff

Antoni Morell Perez
Jose Lopez Vicario
Carles Pedret Ferre
Carles Sanchez Ramos

Group languages

You can consult this information at the end of the document.

Prerequisites

N/A

Objectives

The main objective of this subject is for the student to be able to understand what is the best possible strategy to optimize the treatment of the data to be analyzed. To do this, different techniques will be presented to process the input data (Time Series Analysis, coding in SVM or Random Forest or, in terms of text processing, techniques such as the Bag of Words or LDA). In a more advanced way, the use of techniques such as genetic algorithms or neural networks will be explored. In the optimization part, linear and non-linear methods will be studied, in addition to covering multi-objective optimization methodologies. Finally, advanced decision-making concepts will be introduced, touching on aspects such as the introduction of risk and uncertainty associated with the information to be analyzed.

Learning outcomes

  • CA10 (Design the correct data processing strategy to obtain the expected result.) Design the correct data processing strategy to obtain the expected result.
  • CA11 (Propose a robust decision-making system that considers the associated risk and uncertainty given the available information.) Propose a robust decision-making system that considers the associated risk and uncertainty given the available information.
  • KA14 (Describe the most appropriate data representation techniques for solving a specific problem.) Describe the most appropriate data representation techniques for solving a specific problem.
  • KA15 (Identify appropriate optimisation and decision-making strategies in order to comply with the restrictions of the problem at hand, and obtain optimum algorithm performance.) Identify appropriate optimisation and decision-making strategies in order to comply with the restrictions of the problem at hand, and obtain optimum algorithm performance.
  • SA18 (Apply the most appropriate data manipulation and representation techniques to the problem at hand.) Apply the most appropriate data manipulation and representation techniques to the problem at hand.
  • SA19 (Critically apply optimisation methods to decision-making processes for scientific and engineering problems.) Critically apply optimisation methods to decision-making processes for scientific and engineering problems.
  • SA20 (Formulate decision-making problems so that they can be tackled using numerical methods and offer solutions that incorporate considerations related to risk and uncertainty.) Formulate decision-making problems so that they can be tackled using numerical methods and offer solutions that incorporate considerations related to risk and uncertainty.

Contents

Exploratory data analysis

  • Introduction to Data Processing. Main aplication areas and problems
  • Data representation, feature extraction.
  • Data structure exploration, visualization and clustering
  • Dimensionality reduction and feature selection.
  • Supervised Methods for Data Analysis: SVM, regression.
  • Validation. Metrics, analisis of bias in models, statistical tools, trustworthiness

Optimization

  • linear programming
  • Non-linear optimisation
  • Duality, multipliers, dynamic programming
  • modelling and optimisation software

Multi-objective optimisation

  • Multicriteria decisión making
  • Methods
  • Multicriteria, preferences
  • Uncertainty and risk

Advanced topics and applications

Learning activities and methodology

Title Hours ECTS Learning outcomes
Face-to-face classroom 30 1.2 CA10, CA11, KA14, KA15, SA18, SA19, SA20
Autonomous activity 90 3.6 CA10, CA11, KA14, KA15, SA18, SA19, SA20
Supervised Activity 15 0.6 CA10, CA11, KA14, KA15, SA18, SA19, SA20

This subject has a marked engineer character. Theory: it is rather a methodology, therefore trying to promote methodological application instead of theoretical developments. At the end of the subject, assignments/projects will be presented for evaluation.

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

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Project and exercises 100 15 0.6 CA10, CA11, KA14, KA15, SA18, SA19, SA20

This subject is assessed based on a set of tasks proposed by the instructor, each of which will require both a written report and an oral presentation.

The final grade for the course will be calculated based on the reports and presentations.


Honours Distinction (Matrícula d’Honor). The awarding of an Honours Distinction is solely at the discretion of the instructor(s) responsible for the course. UAB regulations state that Honours Distinctions may only be awarded to students who have obtained a final grade of 9.0 or higher, and the number awarded may not exceed 5% of the total number of enrolled students.


Not assessable. A student will be considered “Not assessable” if they do not submit or participate in any of the tasks proposed by the instructor.


Irregularities by the student, copying and plagiarism

Without prejudice to other disciplinary measures that are deemed appropriate, and in accordance with current academic regulations, irregularities committed by the student that may lead to a variation in the qualification of an act of evaluation. Therefore, plagiarizing, copying or letting any assessment activity be copied will result in failing it with a zero and cannot be recovered in the same academic year. Therefore, this subject will be suspended directly, with no opportunity to recover it in the same course.


Use of Artificial Intelligence tools

Prohibited use: In this subject, the use of Artificial Intelligence (AI) technologies is not allowed in any of its phases. Any work that includes fragments generated with AI will be considered a lack of academic honesty and may lead to a partial or total penalty in the grade of the activity, or greater sanctions in serious cases.

Bibliography

Reference material and sources will be provided in each section

Software

MATLAB

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
(PLABm) Practical laboratories (master) 1 English first semester afternoon