Machine Learning

Credits
6
Types
  • BDMA: Compulsory
  • MDS: Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
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.

Teachers

Person in charge

Others

Weekly hours

Theory
1.9
Problems
0
Laboratory
1.9
Guided learning
0
Autonomous learning
6.86

Competences

Transversal Competences

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

    • Third language

  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

    • Entrepreneurship and innovation

  • CT1 - Know and understand the organization of a company and the sciences that govern its activity; have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit. Being aware of and understanding the mechanisms on which scientific research is based, as well as the mechanisms and instruments for transferring results among socio-economic agents involved in research, development and innovation processes.

    • Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
  • CB10 - 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.

    • Generic Technical Competences

    Generic

  • CG2 - Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats

    • Technical Competences

    Especifics

  • CE6 - Design the Data Science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from both structured and unstructured data and potentially stored in heterogeneous formats.
  • CE7 - Identify the limitations imposed by data quality in a data science problem and apply techniques to smooth their impact
  • CE10 - Identify machine learning and statistical modeling methods to use and apply them rigorously in order to solve a specific data science problem
  • CE12 - Apply data science in multidisciplinary projects to solve problems in new or poorly explored domains from a data science perspective that are economically viable, socially acceptable, and in accordance with current legislation
  • CE13 - Identify the main threats related to ethics and data privacy in a data science project (both in terms of data management and analysis) and develop and implement appropriate measures to mitigate these threats

    • Objectives

    1. Formulate the problem of (machine) learning from data, and know the different machine learning tasks, goals and tools.
      Related competences: CB6, CB7, CB8, CB10,
    2. 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
      Related competences: CT4, CT5, CT1, CG2, CE6, CE7, CE10, CE12, CE13, CB6, CB7, CB8, CB9, CB10,
    3. To be able to solve concrete machine learning problems with available open-source software
      Related competences: CT4, CT5, CG2, CE6, CE7, CE10, CE12, CE13, CB6, CB9,

    Contents

    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.
    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

    Activities

    Activity Evaluation act



    Lab lectures

    Lab lectures
    Objectives: 2 3
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    27h
    Guided learning
    0h
    Autonomous learning
    27h

    Mid-term exam (test)

    Mid-term exam (test)
    Objectives: 1 2
    Week: 7
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final exam

    Final exam
    Objectives: 1 2
    Week: 17
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Course project

    Course project
    Objectives: 1 2 3
    Week: 18
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    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.

    Evaluation methodology

    The course is graded as follows:

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

    final grade = 25% P + 50% F + 25% L

    Bibliography

    Basic

    Complementary

    Web links

    Previous capacities

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