ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 21 Interactive Classroom: 21 Total: 43
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Science and Artificial Intelligence, Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The course introduces the three main paradigms in the field of machine learning: supervised, unsupervised and reinforcement learning. The student will be trained in the generation of prediction models (regression and classification), also considering the possibility of combining different techniques to improve performance. Strategies will also be described to optimize the performance of both learning, by means of preprocessing and feature extraction techniques, and of the models generated, by means of their regularization and evaluation.
Supervised learning. Unsupervised learning. Reinforcement learning. Model combination. Preprocessing and feature extraction techniques, regularization, model building and evaluation.
Básica:
C.M. Bishop. Pattern recognition and machine learning, Springer, 2006, ISBN: 978-0387310732
R.O. Duda, P.E. Hart, D.G. Stork. Pattern classification. Wiley Interscience, 2000. ISBN: 978-0471056690
R.S. Sutton, A.G. Barton. Reinforcement learning: an introduction. MIT Press, 2nd Edition, 2018. ISBN: ISBN: 978-0262039246
Complementaria:
H. Daume. A course in machine learning. Autopublicado, 2017
P. Harrington. Machine learning in action. O'Reilly, 2012. ISBN 978-1617290183
J. Hurwitz, D. Kirsch. Machine learning for dummies. John Wiley & Sons, Inc., 2018. ISBN 9781119454953
BASIC AND GENERAL
GC2 - Successfully tackle all the stages of an Artificial Intelligence project.
GC3 - Search and select useful information needed to solve complex problems, handling with fluency the bibliographic sources of the field.
GC4 - Elaborate adequately and with some originality written compositions or motivated arguments, write plans, work projects, scientific articles and formulate reasonable hypotheses in the field.
CG5 - Work in teams, especially multidisciplinary teams, and be skilled in time management, people and decision making.
CB6 - Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context.
CB7 - That students know how to apply acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.
CB8 - That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments.
CB9 - That students know how to communicate their conclusions and the ultimate knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way.
TRANSVERSALS
CT3 - Use the basic tools of information and communication technologies (ICT) necessary for the exercise of their profession and for lifelong learning.
CT4 - Develop for the exercise of a citizenship respectful of democratic culture, human rights and gender perspective.
CT7 - Develop the ability to work in interdisciplinary or transdisciplinary teams, to offer proposals that contribute to sustainable environmental, economic, political and social development.
CT8 - Value the importance of research, innovation and technological development in the socioeconomic and cultural progress of society.
CT9 - Have the ability to manage time and resources: develop plans, prioritize activities, identify critical ones, set deadlines and meet them.
SPECIFIC
CE10 - Ability to build, validate and apply a stochastic model of a real system from observed data and critical analysis of the results obtained.
SC11 - Understanding and mastery of the main techniques and tools of data analysis, both from the statistical point of view and machine learning, including those dedicated to the treatment of large volumes of data, and ability to select the most appropriate for problem solving.
CE12 - Ability to plan, formulate and solve all stages of a data project, including understanding and mastery of basic fundamentals and techniques for searching and filtering information in large data collections.
CE15 - Knowledge of computer tools in the field of machine learning, and ability to select the most appropriate for solving a problem.
The methodology includes the expository method / lecture, laboratory practices, tutorials, independent work, case studies, project-based learning. It will be carried out with the following training activities:
1) Problem-based learning, seminars, case studies and projects: these are sessions whose objective is that students acquire certain skills based on the resolution of exercises, case studies and projects that require the student to apply the knowledge and skills developed during the course. These sessions may require the student to present orally the solution to the problems posed. The work carried out by the students can be done individually or in work groups.
2) Theory classes: Oral exposition complemented with the use of audiovisual media and the introduction of some questions directed to the students, with the purpose of transmitting knowledge and facilitating learning. In addition to the time of oral exposition by the professor, this formative activity requires the student to dedicate some time to prepare and review on their own the materials object of the class.
3) Practical laboratory classes: classes dedicated to the development of practical work involving the resolution of complex problems, and the analysis and design of solutions that constitute a means for their resolution. This activity may require students to present their work orally. The work done by the students can be done individually or in work groups.
The assessment will consist of two parts:
- Final exam, with weighting of 50% of the final grade.
- Evaluation of practical work, with a weighting of 50% of the final grade.
It will be necessary to reach 40% of the score in each part.
The grade will be not presented when no practical work or final exam is handed in.
Second opportunity
The evaluation will be carried out according to the same criteria described above. A new term will be opened for the delivery of the practical works, in case they had not been delivered in the first opportunity.
A1: Theory classes: 21 classroom hours, 42 hours total dedication.
A2: Practical laboratory classes: 14 classroom hours, 60 hours total dedication.
A3: Problem-based learning, seminars, case studies and projects: 7 classroom hours, 48 hours total dedication.
Weekly study of the subject is recommended
Nicolas Vila Blanco
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881815509
- nicolas.vila [at] usc.es
- Category
- Professor: Temporary supply professor for IT and others
Lara María Vázquez González
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- laram.vazquez [at] usc.es
- Category
- Xunta Pre-doctoral Contract
Olinda Nelly Condori Fernandez
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- n.condori.fernandez [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
| Tuesday | |||
|---|---|---|---|
| 17:30-19:00 | CLIL_01 | English | IA.02 |
| 19:00-20:30 | CLE_01 | English | IA.02 |
| 01.13.2023 17:00-20:45 | CLIL_01 | IA.02 |
| 01.13.2023 17:00-20:45 | CLE_01 | IA.02 |
| 06.21.2023 17:00-20:45 | CLE_01 | Classroom A2 |
| 06.21.2023 17:00-20:45 | CLIL_01 | Classroom A2 |