Machine Learning and Data Processing (2500036) – Course 2025/26 PDF
Syllabus
Learning Objectives
Knowledge about machine learning algorithms and data science. 1 Understand and apply the main machine learning algorithms. 2 Understand the life cycle phases of data science: data mining processes. Supervised (regression and classification), unsupervised (clustering) and semi-supervised learning. Linear regression methods (regression error functions, least squares, notion of regularization, generalized regression). Linear methods by classification (error functions by classification, Bayesian classifiers). Hierarchical methods, general construction of decision trees. Neural networks. Kernel-based methods. Kernelized regularized linear regression, basic kernel functions. Explore the life cycle of data science: questioning, data collection, analysis, visualization, statistical inference, prediction, and decision making. It focuses on quantitative critical thinking and key principles and techniques: languages to transform, query, and analyze data; algorithms for machine learning methods: regression, classification and grouping; principles of informational visualization; measurement and prediction error; and techniques for scalable data processing.
Competencies
Especific
Ability to analyze the problem of safety and health in construction sites. (Common module to the Civil branch)
Knowledge of the typology and calculation bases of prefabricated elements and their application in manufacturing processes. (Specific technology module: Civil Construction)
Knowledge about the project, calculation, construction and maintenance of building works in terms of structure, finishes, facilities and own equipment. (Specific technology module: Civil Construction)
Capacity for the construction and conservation of roads, as well as for the dimensioning, the project and the elements that make up the basic road equipment. (Specific technology module: Civil Construction)
Capacity for the construction and conservation of railway lines with knowledge to apply specific technical regulations and differentiating the characteristics of the mobile material. (Specific technology module: Civil Construction)
Ability to apply construction procedures, construction machinery and construction planning techniques. (Specific technology module: Civil Construction)
Capacity for the construction of geotechnical works. (Specific technology module: Civil Construction)
Total hours of student work
| Hours | Percentage | |||
|---|---|---|---|---|
| Supervised Learning | Large group | 45h | 100.00 % | |
| Self Study | 67.5h | |||
Teaching Methodology
The course consists of 1.5 hours per week of classroom activity (large size group) and 1.5 hours weekly with half the students (medium size group). The 1.5 hours in the large size groups are devoted to theoretical lectures, in which the teacher presents the basic concepts and topics of the subject, shows examples and solves exercises. The 1.5 hours in the medium size groups is devoted to solving practical problems with greater interaction with the students. The objective of these practical exercises is to consolidate the general and specific learning objectives. Support material in the form of a detailed teaching plan is provided using the virtual campus ATENEA: content, program of learning and assessment activities conducted and literature. Although most of the sessions will be given in the language indicated, sessions supported by other occasional guest experts may be held in other languages.
Grading Rules
The evaluation calendar and grading rules will be approved before the start of the course.
The student will be assessed using two types of tests. On the one hand, during the classes there will be several evaluable tests, which the student must deliver at the end of the session; these tests will weigh 40% in the final grade and may include an oral presentation. On the other hand, towards the end of the course the student will have to carry out an evaluation exam. This exam will weigh 60% of the final grade. Criteria of qualification and of admission to the re-evaluation: The students suspended to the ordinary evaluation that have presented regularly in the proofs of evaluation of the asignatura suspended will have option to realize a proof of re-evaluation in the period fixed in the academic calendar. Students who have already passed it or students who have qualified as not presented will not be able to take the re-assessment test for a subject. The maximum grade in the case of taking the re-assessment exam will be five (5.0). The non-attendance of a student summoned to the re-evaluation test, held in the set period may not lead to the performance of another test with a later date. Extraordinary assessments will be carried out for those students who, due to accredited force majeure, have not been able to take any of the continuous assessment tests. These tests must be authorized by the corresponding head of studies, at the request of the teacher responsible for the subject, and will be carried out within the corresponding teaching period.
Test Rules
Failure to perform a laboratory or continuous assessment activity in the scheduled period will result in a mark of zero in that activity.
Bibliography
Basic
- Bishop, C.M. Pattern recognition and machine learning. New York: Springer, 2006. ISBN 0387310738.
- Lee, J.A.; Verleysen, M. Nonlinear dimensionality reduction. New York: Springer, 2007. ISBN 9780387393506.