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 (
)
Others
Oriol Pujol (
)
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.
Objectives
Learn and understand the most common machine learning techniques for unsupervised and supervised tasks.
Related competences:
CT6,
CEA3,
Learn how to solve a problem using machine learning techniques
Related competences:
CT3,
CT6,
CT7,
CEA3,
CEP2,
CEP7,
CG2,
CG4,
Contents
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
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
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
Introduction to ML
Theory
1.5
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0
Theory: Introduction to ML
Cluster Analysis
Theory
1.5
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
0
Factor analysis
Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0
Visualization
Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0
Introduction to supervised learning
Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0
Lazy Learning
Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0
Feature Selection
Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0
Model selection and taxonomy
Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0
Linear Decision
Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0
Kernel Learning
Theory
3
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
0
Ensemble Learning
Theory
3
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
0
Bayesian Learning
Theory
3
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
0
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 if exam>=3,5 and Work>= 4
Each course a and b will be stablished in the following ranges: 0,4 <= a <= 0,7 and 0,3 <= b <= 0,6
Work = 0,25 x W1 + 0,25 x W2 + 0,2 x W3 + 0,2 x W4 + 0,1 x W5