Introduction to Machine Learning

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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

  • Maria Salamó Llorente ( )

Weekly hours

Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
2

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: CEA3, CT6, CB6,
  2. Learn how to solve a problem using machine learning techniques
    Related competences: CEA3, CG2, CG4, CEP2, CEP7, CT3, CT6, CT7,

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
    - 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
15h

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
15h

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
15h

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
15h

Work 5 – (W5) Readings of different research papers

Read and analyse different research papers during the course
Objectives: 1
Week: 15 (Outside class hours)
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
10h

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:

Previous capacities

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