Introduction to Machine Learning

Credits
5
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS;UB
This course provides an introduction on machine learning. It gives an overview of many concepts, techniques and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such support vector machines. The course is divided into three main topics: supervised learning, unsupervised learning, and machine learning theory. Topics include: (i) Supervised learning (linear decision, non linear decision and probabilistic). (ii) Unsupervised learning (clustering, factor analysis, visualization). (iii) Learning theory (bias/variance theory, empirical risk minimization). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to computer vision, medical informatics, and signal analysis.

Teachers

Person in charge

Weekly hours

Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
5.83

Competences

Generic Technical Competences

Generic

  • CG2 - Capability to lead, plan and supervise multidisciplinary teams.
  • CG4 - Capacity for general management, technical management and research projects management, development and innovation in companies and technology centers in the area of Artificial Intelligence.

    • Technical Competences of each Specialization

    Academic

  • CEA3 - Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use on the environment of an intelligent system or service.

    • Professional

  • CEP2 - Capability to solve the decision making problems from different organizations, integrating intelligent tools.
  • CEP7 - Capability to respect the legal rules and deontology in professional practice.

    • Transversal Competences

    Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

    • Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

    • Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

    • 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.

    • Objectives

    1. Learn and understand the most common machine learning techniques for unsupervised and supervised tasks.
      Related competences: CT6, CEA3, CB6,
    2. Learn how to solve a problem using machine learning techniques
      Related competences: CT3, CT6, CT7, CEA3, CEP2, CEP7, CG2, CG4,

    Contents

    1. 1. Introduction to machine learning
      -What is learning?
      -Definition of learning
      -Elements of machine learning
      -Paradigms of machine learning
      -Applications of machine learning
      -Nuts and bolts of machine learning theory
    2. Unsupervised learning
      -Introduction to unsupervised learning
      -Clustering
      -Classification of clustering algorithms: K-Means and EM
      -Factor Analysis : PCA (Principal Components Analysis) and ICA (Independent Component Analysis)
      -Self-Organized Maps (SOM) and Multi-dimensional Scaling
      -Recommender Systems
    3. Supervised learning
      - Introduction and perspectives
      - Lazy Learning
      - Introduction to feature selection
      - Model selection
      - Recommender Systems
      - Supervised learning taxonomy
      - Linear decision
      - Non-linear decision learning: Kernel methods
      - Non-linear decision learning: Ensemble Learning
      - Bayesian Learning

    Activities

    Activity Evaluation act


    Work 1 - (W1) Unsupervised exercise

    Unsupervised exercise related to the techniques studied in this course
    Objectives: 2
    Week: 4
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Work 2 - (W2) Lazy learning exercise

    implement a lazy learning exercise for a particular problem
    Objectives: 2
    Week: 7
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Work 3 - (W3) Kernel Learning exercise

    This exercise is devoted to implement or analyse a Kernel Learning
    Objectives: 2
    Week: 10
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Work 4 – (W4) Non Linear Decision exercise

    This exercise is devoted to implement or analyse Ensemble Learning algorithms
    Objectives: 2
    Week: 13
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Work 5 – (W5) Readings of different research papers

    Read and analyse different research papers during the course
    Objectives: 1
    Week: 15
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Introduction to ML

    Introduction to ML
    • Theory: Introduction to ML

    Theory
    4h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Cluster Analysis

    Cluster Analysis, study of the most common techniques used in machine learning

    Theory
    3h
    Problems
    0h
    Laboratory
    3h
    Guided learning
    0h
    Autonomous learning
    1h

    Factor analysis

    Factor analysis: study of the most common techniques

    Theory
    4h
    Problems
    0h
    Laboratory
    1h
    Guided learning
    0h
    Autonomous learning
    0h

    Visualization

    Study of self-organized maps and multi-dimensional scaling techniques

    Theory
    3h
    Problems
    0h
    Laboratory
    1h
    Guided learning
    0h
    Autonomous learning
    0h

    Introduction to supervised learning

    Introduction to supervised learning

    Theory
    3h
    Problems
    0h
    Laboratory
    1h
    Guided learning
    0h
    Autonomous learning
    0h

    Lazy Learning

    Study of different Lazy Learning techniques

    Theory
    2h
    Problems
    0h
    Laboratory
    1h
    Guided learning
    0h
    Autonomous learning
    0h

    Feature Selection

    Study of Feature Selection techniques applied in machine learning

    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    1h

    Model selection and taxonomy

    Model selection and taxonomy

    Theory
    2h
    Problems
    0h
    Laboratory
    1h
    Guided learning
    0h
    Autonomous learning
    0h

    Linear Decision

    Linear Decision: Algorithms

    Theory
    4h
    Problems
    0h
    Laboratory
    1h
    Guided learning
    0h
    Autonomous learning
    0h

    Kernel Learning

    Kernel Learning

    Theory
    3h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    0h

    Ensemble Learning

    Ensemble Learning

    Theory
    3h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    0h

    Recommender Systems

    Recommender Systems. Objectius. Taxonomy. Elements of the recommendation process. Basic algorithms.

    Theory
    3h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    The class is divided in two parts:

    -Theory (2 hours): introduce the contents of the course
    -Laboratory (1 hour) which includes:

    *Practical exercises related to work deliveries
    *Participatory class where students talk about the readings suggested to go deeper into a subject
    Note: These readings will be included as theory in the final exam

    Evaluation methodology

    The course is divided into two parts:

    Exam: an exam at the end of the term
    Work: Work deliveries during the semester (from W1 to W5)

    Mark = a x Exam + b x Work

    Each course a and b will be stablished in the following ranges: 0,35 <= a <= 0,5 and 0,3 <= b <= 0,6

    Work = c x W1 + d x W2 + e x W3 + f x W4

    Each course c, d, e, and f will be stablished in the following ranges: 0,2 <= {c,e} <= 0,4 and 0,1 <= {d, f} <= 0,2

    Bibliography

    Basic

    Web links

    • For more information visit: https://www.ub.edu/pladocent/?cod_giga=569389&curs=2024&idioma=ENG http://Pla docent UB

    Previous capacities

    It is necessary to have knowledge in programming: Python and Java languages