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(Ohio University)

Ohio University (referred to as OHIO or abbreviated as OU) is a public university located in Athens, Ohio, situated on an 1,800 acre campus. Founded in 1804, it is the oldest university in the Northwest Territory, and ninth oldest public university in the United States.

More information: http://en.wikipedia.org/wiki/Ohio_University

Chapter 3. Elsevier web materials.

Chapter 3. Elsevier web materials.
Perception and attention

Perception and attention

Janusz A. Starzyk Computational Intelligence Based on a course taught by Prof. Randall O'Reilly University of Colorado Prof. Włodzisław Duch Uniwersytet Mikołaja Kopernika and Prof. Oliver University of Connecticut School of Medicine
Chapter 6: Temporal Difference Learning

Chapter 6: Temporal Difference Learning

Objectives of this chapter: Introduce Temporal Difference (TD) learning Focus first on policy evaluation, or prediction, methods Then extend to control methods
Chapter 5: Monte Carlo Methods

Chapter 5: Monte Carlo Methods

Monte Carlo methods learn from complete sample returns Only defined for episodic tasks Monte Carlo methods learn directly from experience On-line: No model necessary and still attains optimality Simulated: No need for a full model
Chapter 3: The Reinforcement Learning Problem

Chapter 3: The Reinforcement Learning Problem

Objectives of this chapter: describe the RL problem we will be studying for the remainder of the course present idealized form of the RL problem for which we have precise theoretical results; introduce key components of the mathematics: value functions and Bellman equations; describe trade-offs between applicability and mathematical tractability.
Chapter 9: Planning and Learning

Chapter 9: Planning and Learning

Objectives of this chapter: Use of environment models Integration of planning and learning methods
Hebbian learning models

Hebbian learning models

Janusz A. Starzyk Computational Intelligence Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. Włodzisława Ducha Uniwersytet Mikołaja Kopernika
Chapter 4: Dynamic Programming

Chapter 4: Dynamic Programming

Objectives of this chapter: Overview of a collection of classical solution methods for MDPs known as dynamic programming (DP) Show how DP can be used to compute value functions, and hence, optimal policies Discuss efficiency and utility of DP
Bioinspired Computing Lecture 6 Artificial Neural Networks: From Multilayer to Recurrent Neural Nets Netta Cohen

Bioinspired Computing Lecture 6 Artificial Neural Networks: From Multilayer to Recurrent Neural Nets Netta Cohen
Chapter 1: Introduction

Chapter 1: Introduction

Psychology Artificial Intelligence Control Theory and Operations Research Artificial Neural Networks Reinforcement Learning (RL) Neuroscience
Language

Language

Janusz A. Starzyk Computational Intelligence Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. Włodzisław Duch Uniwersytet Mikołaja Kopernika
Universal Learning Models

Universal Learning Models

Janusz A. Starzyk Computational Intelligence Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. Włodzisław Duch Uniwersytet Mikołaja Kopernika

Seeing motion : From neural circuits to perceptual decisions
Information processing by the brain

Information processing by the brain

Janusz A. Starzyk Computational Intelligence Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. Włodzisława Ducha Uniwersytet Mikołaja Kopernika
Bio-Inspired Computing Overview & Biased History Based on presentation by Netta Cohen from University of Leeds

Bio-Inspired Computing Overview & Biased History Based on presentation by Netta Cohen from University of Leeds
Perception: Getting Started April 24, 2003

Perception: Getting Started April 24, 2003
Foundation and XACTstepTM Software

Foundation and XACTstepTM Software
Self-organization and error correction

Self-organization and error correction

Janusz A. Starzyk Computational Intelligence Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. Włodzisława Ducha Uniwersytet Mikołaja Kopernika
Chapter 8: Generalization and Function Approximation

Chapter 8: Generalization and Function Approximation

Objectives of this chapter: Look at how experience with a limited part of the state set be used to produce good behavior over a much larger part. Overview of function approximation (FA) methods and how they can be adapted to RL
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