The course covers some basic techniques used in the statistical analysis of networks and systems. It first reviews and extends basic concepts on probability, information theory, and linear algebra. Then it presents basic estimation techniques. Finally, it covers the basic approaches of Machine Learning for regression and classification.
Person in charge
Jorge García Vidal (
Jose Maria Barceló Ordinas (
Technical Competences of each Specialization
Computer networks and distributed systems
CEE2.2 - Capability to understand models, problems and algorithms related to computer networks and to design and evaluate algorithms, protocols and systems that process the complexity of computer communications networks.
Generic Technical Competences
CG4 - Capacity for general and technical management of research, development and innovation projects, in companies and technology centers in the field of Informatics Engineering.
Appropiate attitude towards work
CTR5 - Capability to be motivated by professional achievement and to face new challenges, to have a broad vision of the possibilities of a career in the field of informatics engineering. Capability to be motivated by quality and continuous improvement, and to act strictly on professional development. Capability to adapt to technological or organizational changes. Capacity for working in absence of information and/or with time and/or resources constraints.
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.
The main goal of the course is to develop in the students quantitative modeling skills, based on probabilistic techniques.
Probability axioms, basic combinatorics, random variables, independence and conditional probability, expected values (review, only problems and online material), inequalities (Markov, Chebyshev, Jensen), (weak) Law large numbers, entropy and mutual information. Properties of Gaussian distributions, central limit theorem.
Spectral theorem for symmetric matrices. Positive-definite matrices, quadratic forms. SVD. Curse of dimensionality, High-dimensional spaces. Dimensionality reduction. PCA. Monroe-Penrose pseudo-inverse.
Estimation. Basic Machine Learning techniques for classification and regression
Maximum likelihood and bayesian estimation. Linear regression. Bias-variance tradeoff. Classification.
Graphical models and dynamic systems
Graphical models. Belief propagation. Hidden Markov Models. Kalman filters. Time series
Probability axioms, basic combinatorics, random variables, independence and conditional probability, expected values (review, only problems and online material), inclusion/exclusion, conditional independence, inequalities (Markov, Chebyshev, Jensen), examples: Bernouilli, Binomial, Multinomial, Poisson, (weak) Law large numbers, entropy and mutual information. Density functions, examples: uniform, exponential, Gaussian (review, problems and online material), beta, dirichlet, (eigenvalues/eigenvectors, symmetric, positive definite matrices video), multivariate gaussian, memoryless of exponential distribution. Properties of Gaussian distributions, central limit theorem. Objectives:1 Contents:
The evaluation is based on the development of several projects. Each of the projects will be evaluated (0=
FM = Sum_i (Wi*Mi)
Wi = is the weight of each project i = 1, ... N
Mi = is the mark of each project i = 1, ... N
The number of projects may vary over time, but in general, the following projects are foreseen:
* P1 (25%): Basic probability, information theory, and linear algebra,
* P2 (25%): Estimation, ML and Bayesian approaches
* P3 (25%): Understanding Bias-Variance tradeoff
* P4 (25%): Basic regression and classification
Basic knowledge of probability theory, linear algebra and calculus
No hi ha canvis respecte la guia docent.
No changes regarding "guia docent"
Presencial (normalment el número de alumnes matriculats està per sota 20).
Presential (normally the number of students registered is under 20)
El mateix que el proposat a la guia docent.
No changes regarding "guia docent"
Es prepararien materials online per tota la assignatura. Classes per google meet per revisió dels conceptes més complexes i per problemes i dubtes (2 hores de classes per google meet a la setmana).
We would post materials online for all the subject. Classes for google meet for review of the most complex concepts and for problems and questions (2 hours of classes for google meet per week)
Where we are
B6 Building Campus Nord
C/Jordi Girona Salgado,1-3
08034 BARCELONA Spain
Tel: (+34) 93 401 70 00