Checking date: 30/05/2022


Course: 2022/2023

Machine Learning
(18286)
Study: Bachelor in Applied Mathematics and Computing (362)


Coordinating teacher: FUENTETAJA PIZAN, RAQUEL

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming (Year 1 - Semester 1) Probability (Year 2 - Semester 2) Aritificial Intelligence (Year 2 - Semester 2)
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 and inductive learning 2. Classification and prediction techniques 3. Non supervised techniques 4. Reinforcement based techniques 5. Relational learning 6. Methodological aspects
Learning activities and methodology
AF1.THEORETICAL-PRACTICAL CLASSES. Knowledge and concepts students must acquire. Student receive course notes and will have basic reference texts to facilitatefollowing the classes and carrying out follow up work.Students partake in exercises to resolve practical problems and participatein workshops and an evaluation tests, all geared towards acquiring the necessary capabilities.Subjects with 6 ECTS are44 hours as a general rule/ 100% classroom instruction AF2.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. AF3.STUDENT INDIVIDUAL WORK OR GROUP WORK.Subjects with 6 credits have 98 hours/0% on-site. AF8.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. MD1.THEORY CLASS. Classroom presentations by the teacher with IT and audiovisual support in which the subject`s main concepts are developed, while providing material and bibliography to complement student learning. MD2.PRACTICAL CLASS. Resolution of practical cases and problem, posed by the teacher, and carried out individually or in a group. MD3.TUTORING SESSIONS. Individualized attendance (individual tutoring sessions) or in-group (group tutoring sessions) for students with teacher as tutor. Subjects with 6 credits have 4 hours of tutoring/100% on-site. MD6.LABORATORY PRACTICAL SESSIONS. Applied/experimental learning/teaching in workshops and laboratories under the tutor's supervision.
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.