University of Colorado Boulder
Mind and Machine Specialization
University of Colorado Boulder

Mind and Machine Specialization

Taught in English

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David Quigley

Instructor: David Quigley

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.3

(123 reviews)

Beginner level
No prior experience required
1 month at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Develop connections between our understanding of human minds and artificial minds in the field of cognitive science.

  • Discover and synthesize research in various disciplines related to cognitive science (e.g. computer science, psychology, neuroscience).

  • Connect cognitive science principles and ideas to your own understanding.

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.3

(123 reviews)

Beginner level
No prior experience required
1 month at 10 hours a week
Flexible schedule
Learn at your own pace

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  • Develop a deep understanding of key concepts
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Specialization - 4 course series

What you'll learn

  • Describe the details of the Turing test, including its purpose, limitations, and potential impact.

  • Describe the details of Searle’s Chinese Room thought experiment, including its purpose, limitations, and potential impact.

  • Discuss previous and current attempts to create artificial systems that can pass the Turing Test in various domains.

  • Compute and outline the limitations of exponential and factorial growth functions.

Methods for Solving Problems

Course 26 hours4.4 (165 ratings)

What you'll learn

  • Ascribe human and computational problems to various definitions of difficulty and problem solving techniques.

  • Use problem solving strategies to break down problems.

  • Describe the effects of various sources of bias in judgement and decision making.

  • Analyze and apply heuristic models and probability modeling to a variety of decision making scenarios.

Computational Vision

Course 38 hours4.4 (62 ratings)

What you'll learn

  • Apply various models of human and machine vision and discuss their limitations.

  • Demonstrate the geon model of object recognition and its limitations.

  • Argue the benefits and drawbacks of the symbolist and visualist perspectives of mental imagery.

  • Recognize the single layer and multi-layer perceptron neural network models of artificial intelligence.

What you'll learn

  • Model the effects of multiple decision maker systems in various scenarios.

  • Analyze the Prisoner’s Dilemma and other Game Theory scenarios as a model of decision making and behavioral adaptation.

  • Discuss the relationships in genetic evolution models and developmental models of judgement and decision making.

  • Implement the design of autonomous agents as artificial life.

Instructor

David Quigley
University of Colorado Boulder
4 Courses22,549 learners

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