Checking date: 20/05/2022


Course: 2022/2023

Machine learning I
(16492)
Study: Bachelor in Data Science and Engineering (350)


Coordinating teacher: FUENTETAJA PIZAN, RAQUEL

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming Probability and Data Analysis
Objectives
* Understand basic Machine Learning techniques * Learn to determine when to use Machine Learning on real problems * Learn to determine which technique is appropriate for each problem * Learn to apply the techniques in a practical way to real problems Competences: CB1: The students must demonstrate to understand knowledge in an area of study which origin is the secondary education, and will be in a level that, supported with books and other bibliographic references, includes aspects in the frontiers of knowledge. CB2: The students know to apply their knowledge to their work in a professional way and own the competences usually required to solve problems in its area of study CB3: The students own the capacity to interpret relevant data to elaborate claims that include an analysis in social, scientific and ethics topics CB4: The student can transmit information, ideas, problems and solutions to both specialized and non-specialized audience. CB5: Students have developed the learning capabilities to begin new studies with a high degree of autonomy CE13: Capacity to apply and design machine learning methods in classification, regression and clustering for tasks in supervised, unsupervised and reinforcement learning CE2: Capacity to correctly identify predictive problems for a given data and goals, and use the basic results of the regression analysis as a fundamental predictive method. CE3: Capacity to correctly identify classification problems associated to specific goals and data, and to use the results of multivariate analysis as a basics of the classification, clustering and dimensionality reduction methods CG1: Knowledge and abilities to analyze and synthesize basic problems related with engineering and data science, and to solve them and report the results CG2: Knowledge of basic scientific and technical topics that enable for learning new methods and technologies CG3: Capability to solve problems with initiative, decision making, creativity, and communication skills, understanding the ethical, social and professional responsabilities of the data management. Leading, innovation and entrepreneurship capabilities. CG4: Capability to solve technological, computing, mathmatical and statistic problems which can arise in engineering and data science. CG5: Capability to solve problems formalized mathematically and applied to different topics, using numeric algorithms and computational methods. CG6: Capability to synthesize conclusions obtained from performed analysis, and to report in a clear and convincing way, both orally and written. RA1: Advanced knowledge and comprehensiono of the theoreticat and practical aspects of the working methodology in the area of data science with a depth that close to the frontier of knowledge RA2: Capability to apply knowledge in complex working environments and specialized areas which require the use of creative and innovative ideas. RA3: To have the capability to collect and understand data and information over which to create conclusions including, when needed, a reflection about social, ethic or scientific issues. RA4: To be able to manage complex situations which require the development of new solutions both in academic and professional environments in its area of study RA5: To know how communicate to different audiences knowledge, methodologies, ideas, problems and solutions in a clear and precise way RA6: Be able to identify his/her own formative requirements in is area of study or professional environment, and to organize its own learning process with a high degree of autonomy in different contexts.
Skills and learning outcomes
Description of contents: programme
· Introduction to machine learning · Learning decision trees and rules · Methodological aspects · Learning regression trees and rules · Ensembles of learning methods · Frequent itemsets and association rules · Reinforcement learning · Relational learning
Learning activities and methodology
AF1: Presential classes, with theoretical and practical contents AF3: Student work AF8: Practical labs. AF9: Final exam. Evaluation of the abilities that have been acquired along the course. MD1: Classes with theoretical contents MD2: Practices, with cases and problems MD3: Individual and group tutories MD6: Lab practices with support of assistant
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Calendar of Continuous assessment
Basic Bibliography
  • E. Rich y K. Knight. Artificial Intelligence. McGraw-Hill.
  • S. Russel y P. Norving. Artificial Intelligence: a modern approach. Prentice Hall. 2003
  • T. M. Mitchell. Machine Learning. Mc Graw Hill.
Additional Bibliography
  • J. W. Shavlik y T. G. Dietterich (eds.). Readings in Machine Learning. Morgan Kaufmann.
  • P. W. Langley. Elements of Machine Learning. Morgan Kaufmann.
  • R. Sutton and A Barto. Reinforcement Learning: an Introduction. Kluwer Academic Publishers.
  • Saso Dzeroski y Nada Lavrac. Relational Data Mining. Springer Verlag.

The course syllabus may change due academic events or other reasons.