Aprendizaje Automático

Créditos
6
Tipos
Obligatoria de especialidad (Ciencia de los Datos)
Requisitos
Esta asignatura no tiene requisitos, pero tiene capacidades previas
Departamento
CS
The aim of machine learning is the development of theories, techniques and algorithms to allow a computer system to modify its behavior in a given environment through inductive inference. The goal is to infer practical solutions to difficult problems --for which a direct approach is not feasible-- based on observed data about a phenomenon or process. Machine learning is a meeting point of different disciplines: statistics, optimization and algorithmics, among others.

The course is divided into conceptual parts, corresponding to several kinds of fundamental tasks: supervised learning (classification and regression) and unsupervised learning (clustering, density estimation). Specific modelling techniques studied include artificial neural networks and support vector machines. An additional goal is getting acquainted with python and its powerful machine learning libraries.

Profesorado

Responsable

  • Mario Martín Muñoz ( )
  • Marta Arias Vicente ( )

Otros

  • Bernat Coma Puig ( )

Horas semanales

Teoría
1.9
Problemas
0
Laboratorio
1.9
Aprendizaje dirigido
0
Aprendizaje autónomo
6.86

Competencias

Competencias Técnicas Genéricas

Genéricas

  • CG1 - Capacidad para aplicar el método científico en el estudio y análisis de fenómenos y sistemas en cualquier ámbito de la Informática, así como en la concepción, diseño e implantación de soluciones informáticas innovadoras y originales.
  • CG3 - Capacidad para el modelado matemático, cálculo y diseño experimental en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación e innovación en todos los ámbitos de la Informática.
  • CG5 - Capacidad para aplicar soluciones innovadoras y realizar avances en el conocimiento que exploten los nuevos paradigmas de la Informática, particularmente en entornos distribuidos.

Competencias Transversales

Razonamiento

  • CTR6 - Capacidad de razonamiento crítico, lógico y matemático. Capacidad para resolver problemas dentro de su área de estudio. Capacidad de abstracción: capacidad de crear y utilizar modelos que reflejen situaciones reales. Capacidad de diseñar y realizar experimentos sencillos, y analizar e interpretar sus resultados. Capacidad de análisis, síntesis y evaluación.

Básicas

  • CB6 - Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios (o multidisciplinares) relacionados con su área de estudio.

Competencias Técnicas de cada especialidad

Específicas comunes

  • CEC1 - Capacidad para aplicar el método científico en el estudio y análisis de fenómenos y sistemas en cualquier ámbito de la Informática, así como en la concepción, diseño e implantación de soluciones informáticas innovadoras y originales.
  • CEC2 - Capacidad para el modelado matemático, cálculo y diseño experimental en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación e innovación en todos los ámbitos de la Informática.

Objetivos

  1. Formulate the problem of (machine) learning from data, and know the different machine learning tasks, goals and tools.
    Competencias relacionadas: CG3, CEC1,
  2. Organize the workflow for solving a machine learning problem, analyzing the possible options and choosing the most appropriate to the problem at hand
    Competencias relacionadas: CB6, CEC1, CEC2, CTR6, CG5,
  3. Ability to decide, defend and criticize a solution to a machine learning problem, arguing the strengths and weaknesses of the approach. Additionally, ability to compare, judge and interpret a set of results after making a hypothesis about a machine learning problem
    Competencias relacionadas: CG1, CEC1, CEC2, CTR6,
  4. Understand and know how to apply least squares techniques for solving supervised learning problems
    Competencias relacionadas: CG3, CEC2, CTR6,
  5. Understand and know how to apply techniques for single and multilayer neural networks for solving supervised learning problems
    Competencias relacionadas: CG3, CB6, CEC2, CTR6,
  6. Understand and know how to apply support vector machines for solving supervised learning problems
    Competencias relacionadas: CG3, CB6, CEC2, CTR6, CG5,
  7. Understand and formulate different theoretical tools for the analysis, study and description of machine learning systems
    Competencias relacionadas: CG3, CTR6, CG5,
  8. Understand and know how to apply the basic techniques for solving unsupervised learning problems
    Competencias relacionadas: CG3, CB6,

Contenidos

  1. Introduction to Machine Learning
    General information and basic concepts. Overview to the problems tackled by machine learning techniques. Supervised learning (classification and regression), unsupervised learning (clustering and density estimation) and semi-supervised learning (reinforcement and transductive). Examples.

    General information and basic concepts. Overview to the problems tackled by machine learning techniques. Supervised learning (classification and regression), unsupervised learning (clustering and density estimation) and semi-supervised learning (reinforcement and transductive). Examples.

    General information and basic concepts. Overview to the problems tackled by machine learning techniques. Supervised learning (classification and regression), unsupervised learning (clustering and density estimation) and semi-supervised learning (reinforcement and transductive). Examples.
  2. Supervised machine learning theory
    The supervised Machine Learning problem setup. Classification and regression problems. Bias-variance tradeoff. Regularization. Overfitting and underfitting. Model selection and resampling methods.
  3. Linear methods for regression
    Error functions for regression. Least squares: analytical and iterative methods. Regularized least squares. The Delta rule. Examples.
  4. Linear methods for classification
    Error functions for classification. The perceptron algorithm. Novikoff's theorem. Separations with maximum margin. Generative learning algorithms and Gaussian discriminant analysis. Naive Bayes. Logistic regression. Multinomial regression.
  5. Artificial neural networks
    Artificial neural networks: multilayer perceptron and a peak into deep learning. Application to classification and to regression problems.
  6. Kernel functions and support vector machines
    Definition and properties of Kernel functions. Support vector machines for classification and regression problems.
  7. Unsupervised machine learning
    Unsupervised machine learning techniques. Clustering algorithms: EM algorithm and k-means algorithm.
  8. Ensemble methods
    Bagging and boosting methods, with an emphasis on Random Forests

Actividades

Actividad Acto evaluativo




Mid-term exam (test)


Objetivos: 1 2 3
Semana: 7
Tipo: examen de teoría
Teoría
1h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
8h

Final exam


Objetivos: 1 2 3 4 5 6 7 8
Semana: 17
Tipo: examen final
Teoría
2h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
16h

Course project


Objetivos: 1 2 3 4 5 6 7 8
Semana: 18
Tipo: entrega
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
25h

Metodología docente

The course introduces the most important concepts in machine learning and its most relevant techniques with a solid foundation in math. All the theory and concepts are illustrated and accompanied by real-world examples and code using open source libraries.

The theory is introduced in lectures where the teacher exposes the concepts, and during the lab sessions students will see many examples on how to apply the methods and theory learned, as well as code their own solutions to exercises proposed by the teacher.

Students have to work on a course project using a real-world dataset.

Método de evaluación

The course is graded as follows:

P = Grade of mid-term test-type exam
F = Score of the final exam
L = Score for the practical work

final grade = 20% P + 40% F + 40% L

Bibliografía

Básica:

Complementaria:

Web links

Capacidades previas

Elementary notions of probability and statistics.
Elementary linear algebra and real analysis
Good programming skills in a high-level language