Competencies and objectives

 

Course context for academic year 2024-25

Machine Learning is based on inference from examples to learn models without being explicitly programmed with a fixed set of rules.

The process is divided into a first learning phase where a model learns patterns and relationships between variables from examples (data) to then be used in a second phase to make predictions or decisions about new data.

There are several types of techniques such as supervised, unsupervised, and reinforcement learning. Supervised learning involves the use of labeled data to train the model, while unsupervised learning is used to find patterns and structures in unlabeled data. Reinforcement learning involves the algorithm learning through a trial and error process, receiving positive or negative rewards based on its behavior.

These techniques are applied in a wide variety of fields such as manufacturing, sales, health, travel and accommodations, financial services, and energy, among others. Currently, the fields of application and problems that they are able to solve are expanding.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25

Transversal Competences

  • CT1 : Be able to lead projects related to artificial intelligence, as well as to manage work teams.
  • CT2 : Demonstrate computer and information skills in the field of artificial intelligence.
  • CT3 : Show oral and written communication skills.

 

General Competences

  • CG1 : Apply the knowledge acquired to real problems related to artificial intelligence.
  • CG10 : Be able to use engineering principles and modern computer technologies to research, design and implement new applications of artificial intelligence,
  • CG2 : Be able to develop and learn in a self-directed or autonomous way topics related to artificial intelligence.
  • CG5 : Know how to manage the available information and resources related to artificial intelligence.
  • CG6 : Being able to adapt to environments related to artificial intelligence, fostering teamwork, creativity,
  • CG7 : To be able to adapt to the constant evolution of the discipline and to understand and apply new technical and scientific developments related to artificial intelligence.
  • CG8 : Knowing how to plan, design, develop, implement and maintain products, applications and services related to artificial intelligence, taking into account technical, economic and efficiency aspects.

 

Specific Competences

  • CE08 : In-depth knowledge of machine learning technologies, algorithms and tools (including supervised, unsupervised or reinforced learning).
  • CE09 : Learn to use metrics and techniques for the validation and comparison of the results of machine learning methods.

 

Basic Competences

  • CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
  • CB6 : Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
  • CB7 : That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ¿¿study
  • CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
  • CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.

 

 

 

Learning outcomes (Training objectives)

- Understand the fundamental concepts of machine learning.

- Understand the phases of data processing, feature selection and evaluation methods of a classification system.

- Describe the main architectures used in machine learning, as well as the most typical applications.

- Identify the most appropriate type of machine learning algorithm for various types of problems in different domains.

- Implement machine learning algorithms using different tools.

 

 

Specific objectives stated by the academic staff for academic year 2024-25

  • To understand the feature extraction process.
  • To understand and address issues associated with data collection.
  • To understand and comprehend different methods of supervised, unsupervised, and reinforcement learning.
  • To understand and interpret the paradigm and structure of a conventional classifier.
  • To be able to formulate and solve a problem based on machine learning.

 

 

General

Code: 43504
Lecturer responsible:
Rico Juan, Juan Ramón
Credits ECTS: 4,50
Theoretical credits: 0,90
Practical credits: 0,90
Distance-base hours: 2,70

Departments involved

  • Dept: LANGUAGES AND COMPUTING SYSTEMS
    Area: LANGUAGES AND COMPUTING SYSTEMS
    Theoretical credits: 0,9
    Practical credits: 0,9
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught