Learn effective problem-solving techniques combining computational methods with human decision-making strategies.
Learn effective problem-solving techniques combining computational methods with human decision-making strategies.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mind and Machine Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.4
(167 ratings)
9,236 already enrolled
Instructors:
English
What you'll learn
Analyze and categorize different types of problems
Apply systematic problem-solving strategies
Understand cognitive biases in decision making
Use heuristics and probability models effectively
Skills you'll gain
This course includes:
2.5 Hours PreRecorded video
7 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This foundational course explores various approaches to problem-solving, combining computational methods with human decision-making strategies. Students learn to categorize problems, understand solvability limitations, and apply appropriate solving techniques. The curriculum covers logical problem-solving, heuristic approaches, judgment and decision-making biases, and probability modeling. Through practical examples and interactive discussions, participants develop a comprehensive toolkit for addressing complex problems.
Problems for Minds and Machines
Module 1 · 2 Hours to complete
Computers and Logic
Module 2 · 1 Hours to complete
Humans and Heuristics
Module 3 · 2 Hours to complete
Course Assessment & Wrap-Up Discussion
Module 4 · 35 Minutes to complete
Fee Structure
Instructor
Research Associate
Dr. David Quigley is a Research Associate in the Institute of Cognitive Science and an Assistant Professor - Adjunct in the Department of Computer Science at the University of Colorado Boulder. His research focuses on applying learning analytics techniques to develop machine learning models that analyze student activity and understanding in science classrooms. Dr. Quigley earned his Ph.D. from CU Boulder, where he contributed to projects such as the Inquiry Hub Research-Practice Partnership and the Chicago City of Learning initiative.Prior to his doctoral studies, Dr. Quigley completed his undergraduate and master’s degrees at Georgia Tech, working with the Contextual Computing Group on various projects related to human-computer interaction and educational technology.At CU Boulder, he teaches courses including "Computational Vision," "Interpersonal, Developmental, and Evolutionary Perspectives of the Mind," "Methods for Solving Problems," and "What is 'the mind' and what is artificial intelligence?" His work not only enhances educational practices but also contributes to a deeper understanding of how technology can support learning in science education.Dr. Quigley's contributions to the field of cognitive science and education technology position him as a key figure in advancing research on learning analytics and its practical applications in classroom settings.
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4.4 course rating
167 ratings
Frequently asked questions
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