Checking date: 20/05/2022


Course: 2022/2023

Automatic machine learning
(19101)
Study: Bachelor in Robotics Engineering (381)


Coordinating teacher: FUENTETAJA PIZAN, RAQUEL

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

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming Statistics
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
Skills and learning outcomes
Description of contents: programme
1. Introduction to machine learning 2. Classification and prediction techniques 3. Non supervised techniques 4. Reinforcement-based techniques 5. Relational learning 6. Methodological aspects
Learning activities and methodology
THEORETICAL PRACTICAL CLASSES. Knowledge and concepts students must acquire. Receive course notes and will have basic reference texts. Students partake in exercises to resolve practical problems. TUTORING SESSIONS. Individualized attendance (individual tutoring) or in-group (group tutoring) for students with a teacher. Subjects with 6 credits have 4 hours of tutoring/ 100% on- site attendance. STUDENT INDIVIDUAL WORK OR GROUP WORK. Subjects with 6 credits have 98 hours/0% on-site. WORKSHOPS AND LABORATORY SESSIONS. Subjects with 3 credits have 4 hours with 100% on-site instruction. Subjects with 6 credits have 8 hours/100% on-site instruction.
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.