Pattern Theory and Applications

This is the main page of a graduate-level course in pattern theory, machine learning, pattern formation, pattern recognition and computer vision being taught in 2013/1 at the Polytechnic Institute IPRJ/UERJ. It is generally useful for computer scientists, statisticians, and applied mathematicians.

General Info

• Instructor: prof. Ricardo Fabbri, Ph.D. Brown University
• Meeting times: Tues 12:30pm-2pm, Thursdays 2:20pm - 4pm
• Evaluation criteria: 1 quizz at the end of the term (60%), plus practical projects (40%).
• Forum for file exchange and discussion: uerj.tk

Pre-requisites

• Linux - intermediate to advanced (will be reviewed as needed) - read Recommended Reading
• C/C++ - intermediate to advanced (will be reviewed) - read Recommended Reading
  • Configuring Ubuntu for Programming
• Basic understanding of computer architecture - read "Computer Systems: A Programmer's perspective" listed at Recommended Reading.

Approximate Content

The course focuses on software techniques for parallel computing. We are aiming at a comprehensive treatment on different types of practical parallel programming techniques

• process-oriented parallel programming
• thread programming/thread safety
• single-core vector instructions
• multi-processor and multi-core programming
• mapreduce/hadoop
• MPI
• Cuda/OpenCL
• Dataflow techniques

Each of the above techniques would merit a course of their own, as done in many of the best universities. Therefore we aim at attaining a practical familiarity with each, in the first half of the course, and we will specialize in the later part of the course as to help the students' graduate research.

Main Resources

Lectures

Partial listing & Tentative Outline

1. Overview of pattern theory and classic pattern recognition

Keywords

Portuguese: Teoria dos Padrões, Reconhecimento de Padrões, Visão Computacional, Inteligência Artificial, Formação de Padrões