Mudanças entre as edições de "Stochastic Processes"

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<li style="display: inline-block;"> [[File:Sampling-motion-planning.png|thumb|none|x180px|Application to path planning in robotics [https://natanaso.github.io/ece276b/ref/ECE276B_1_MC.pdf (see this)] ]] </li>
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<li style="display: inline-block;"> [[File:Sampling-motion-planning.png|thumb|none|x180px|Application to path planning for autonomos cars [https://natanaso.github.io/ece276b/ref/ECE276B_1_MC.pdf (see this)] ]] </li>
 
<li style="display: inline-block;"> [[File:Sampling-motion-planning2.png|thumb|none|x180px|Application to robot path planning with obstacles [https://natanaso.github.io/ece276b/ref/ECE276B_1_MC.pdf (see this)] ]] </li>
 
<li style="display: inline-block;"> [[File:Sampling-motion-planning2.png|thumb|none|x180px|Application to robot path planning with obstacles [https://natanaso.github.io/ece276b/ref/ECE276B_1_MC.pdf (see this)] ]] </li>
 
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== Approximate Content ==
 
== Approximate Content ==
This year's course will focus on a modern approach bridging theory and practice.
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This year's course will focus on a '''modern''' approach bridging theory and practice.
As engineers and scientists, you should not learn theory here without also considering broader applications. Recent applications in artificial intelligence, machine learning, robotics, autonomous driving, material science and other topics will be considered. These applications are often too hard to tackle with only this course, but having contact with them will help motivate the abstract theory.
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As engineers and scientists, you should not learn theory here without also considering broader applications. Recent applications in artificial intelligence, machine learning, robotics, autonomous driving, material science and other topics will be considered. These applications are often too hard to tackle at the level of this course, but having contact with them will help motivate the abstract theory. We will try to focus on '''key concepts''' and more realistic applications than most courses (that come from the 1900's), that will prompt us to elaborate theory.
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== Main Resources ==
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=== Textbooks ===
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* Main book:

Edição das 08h07min de 10 de maio de 2018

This is the main page of an undergraduate-level course in stochastic processes, being taught in 2018 (2017 semester 2) at the Polytechnic Institute IPRJ/UERJ.

  • Course pages for previous years: 2012
Recent application of Gaussian stochastic processes for 3D motion capture transfer (CVPR 2017)


  • Application to path planning for autonomos cars (see this)
  • Application to robot path planning with obstacles (see this)

General Info

  • Instructor: prof. Ricardo Fabbri, Ph.D. Brown University
  • Meeting times: Tuesdays 1:20pm-3:10pm Thursdays 1:20pm - 3:10pm, room (?)
  • Forum for file exchange and discussion: email and IRC #labmacambira for chat

Pre-requisites

  • Undergraduate-level mathematics and probability (will review as needed)
  • Desirable: Intermediate programming experience with any numerics scripting language such as Scilab, Python, R or Matlab. Knowing at least one of them will help you learn any new language needed in the course.

Approximate Content

This year's course will focus on a modern approach bridging theory and practice. As engineers and scientists, you should not learn theory here without also considering broader applications. Recent applications in artificial intelligence, machine learning, robotics, autonomous driving, material science and other topics will be considered. These applications are often too hard to tackle at the level of this course, but having contact with them will help motivate the abstract theory. We will try to focus on key concepts and more realistic applications than most courses (that come from the 1900's), that will prompt us to elaborate theory.

Main Resources

Textbooks

  • Main book: