TUTORIALES MICAI 2009

Transcripción

1 ARTIFICIAL INTELLIGENCE AND HEURISTIC METHODS IN BIOLOGY Dr. RAMÓN GARDUÑO JUÁREZ Instituto de Ciencias Físicas, UNAM, México Monday, November 9, :30am Artificial Intelligence (AI) and heuristic methods are extremely important for the present and future developments of bioinformatics, a very recent and strategic discipline having the potential for a revolutionary impact on biotechnology, pharmacology, and medicine. Of particular interest is the Protein Structure Prediction Problem, a NP complete problem, which will be discussed in terms of different AI methods. Outline Duration Topic Description 30 min. 1. Review of protein structure 30 min. 2. Review of the protein structure prediction problem (PSPP) 30 min. 3. Review of PSPP importance in health related issues 30 min. 4. Review of heuristic methods applied to PSPP (SA, Tabu) 1 hour 5. Review of evolutionary methods applied to PSPP (GA) 1 hour 6. Review of artificial intelligence methods applied to PSPP i. Ant Colony Optimization ii. Particle Swarm Optimization Target audience and pre-requisites Those attendants interested in applications of artificial intelligence methods to biological problems, particularly to the protein folding problem. No previous knowledge of biochemistry is required.

2 Procesamiento Automático de Lenguaje Natural Dr. Alexander Gelbukh CIC, IPN, México Monday, November 9, :00pm

3 Software Engineering: principles, methods and approaches in study. Dr. Jaime Muños Arteaga Universidad Autónoma de Aguascalientes, Aguascalientes, México Lunes 9 de Noviembre :00pm Software Engineering is concerned with the theories, methods and tools that are necessary to develop, operate and maintain software with quality in order to offer an effective support to users activities. The use of software is required in different areas of science and the technology, then, it is important to identify what are the foundations of software products. The goal of this tutorial is a hands-on introduction to the theories and approaches behind a software product. To this end, the tutorial begins with the current underlying and challenges in software engineering and discuses many different real scenarios. The second part describes different solutions about emphasizing in the software process: process, people, and products. Furthermore, successful experiences gained form real-world projects are presented, which clearly show the necessity and benefits of this area. The last part presents the emerging topics in software engineering such as web engineering, rolling engineering, software cloud computing etc. Audience The audience for this tutorial is for MICAI 09 participants (students, teacher, researcher etc), in particular artificial intelligence community uses and produces software products, moreover software engineering techniques need to be adapted to work for all and solve your problems.

4 An Introduction to the Use of Artificial Neural Networks Dr. Pilar Gómez Gil INAOE, México Tuesday, November :30am The main objective of this tutorial is to introduce the attendants to basic concepts related to the use of Artificial Neural Networks (ANN), when used as tools in the solution of some scientific and engineering problems. This tutorial covers the main concepts related to ANN models, a brief review of the kind of problems that ANN may tackle and some examples of their applications. This tutorial is directed to people with no knowledge about ANN. Outline Duration Topic Description 1 hour 1. Artificial Neural Networks. What is that? 1.1 Some definitions. 1.2 Advantages and drawbacks of ANN. 1.3 Characteristics of solutions using ANN. 1 hour 2. Basic concepts 2.1 The fundamental neuron. 2.2 The concept of learning by examples 2.2 Some ANN architectures. 2.3 Other interesting (and more complex) architectures of ANN 1 hour 3. Solutions using ANN 3.1 ANN as classifiers. 3.2 ANN as function approximators. 3.3 ANN as predictors. 1 hour 4. Examples using Matlab ANN toolbox 4.1 A very simple classifier. 4.2 A very simple function approximator.

5 Teoría y Práctica del Descubrimiento de Conocimiento en Bases de Datos Dr. Manuel Mejía Lavalle mlavalle@iie.org.mx IIE, México Martes 10 de Noviembre :30am Resumen Que el participante conozca y aplique las técnicas de Minería de Datos más conocidas para atacar problemas en donde se requiere encontrar conocimiento relevante a partir de grandes volúmenes de datos. Para ello el instructor introducirá los conceptos importantes en Minería de Datos y Descubrimiento de Conocimiento, con el propósito de que los participantes se adentren en el contexto del curso. Después presentará ejemplos representativos y de éxito, de tal manera que los participantes perciban de forma concreta la manera en que la Minería de Datos se está aplicando en el mundo real, y vayan visualizando cómo ellos podrían usar la técnica en su área. Luego el instructor reseñará la forma en que operan los algoritmos más útiles de Minería de Datos, para que los participantes entiendan cómo las técnicas trabajan sobre los datos. A continuación los participantes serán involucrados en ejercicios de Minería de Datos, de tal suerte que experimenten directamente con las técnicas de Minería y observen los resultados que se pueden obtener. El instructor cerrará el curso subrayando lo más relevante y analizará el futuro de la Minería de Datos. Contenido Duración Tema Descripción 1 1/2 hora 1. Conceptos 1.1 Introducción. 1.2 Ejemplos básicos. 1.3 Casos de éxito. 1.4 Algoritmos más conocidos. 1 1/2 hora 2. Aplicaciones 2.1 Introducción. 2.2 Desarrollo del Caso A. 2.2 Desarrollo del Caso B. 1 hora 3. Discusión 3.1 Conclusiones 3.2 El futuro

6 Satellite Image Analysis Dr. Jesús Antonio González Bernal M.S. Jorge Morales Cruz INAOE, México Tuesday, November 10, :00pm Satellite image analysis has become an important research area with applications in different aspects of life, such as natural disasters prevention, land cover identification, and natural resources protection. In this tutorial we will describe how remote sensing data is obtained, pre-processed, and analyzed using different techniques. We will also show how to process satellite images with the IDRISI TAIGA GIS and Image Processing software. Outline Duration Topic Description 1 hour 1. Satellite Image Classification 1.1 Remote sensing data 1.2 Satellite image preprocessing 1.3 Satellite images classification 1.4 Spatial data mining 1 hour 2. IDRISI TAIGA 2.1 Introduction to IDRISI 1 hour 3. GIS analysis with IDRISI 3.1 Introduction to GIS analysis 3.2 Exercises 1 hour 4. Satellite images classification with IDRISI 4.1 Introduction to satellite image classification with IDRISI 4.2 Exercises