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