|Introduction to Artificial Intelligence and Machine Learning for Behavior Analysts: A Hands-On Workshop
|Friday, May 22, 2020
|8:00 AM–3:00 PM
|To Be Determined
|Area: PCH; Domain: Applied Research
|CE Instructor: Marc J. Lanovaz, Ph.D.
|MARC J. LANOVAZ (Université de Montréal)
|Description: Artificial intelligence holds promise in revolutionizing how we work and interact. However, the adoption of artificial intelligence in behavior analytic practice and research has been lagging in comparison to other professions (e.g., medicine, psychology). Behavior analysts are not traditionally trained to program and work with automated algorithms, which may explain why we are falling behind. The purpose of the workshop is to provide a gentle introduction to artificial intelligence and some its applications for behavior analysts with no prior training in programming or computer science. First, the presenter will review the terminology and logic underlying artificial intelligence and its application to socially significant problems. Second, the participants will apply machine learning algorithms to solve two behavior analytic problems: 1) the automated detection of vocal stereotypy in children with autism spectrum disorders and 2) the analysis of single-case designs. In summary, the workshop will provide step-by-step written and oral instructions as to how to apply the algorithms to behavior analytic problems, the presenter will model how to train machine learning models, and the participants will practice their newly learned skills by coding their own models (using helper functions) and receive feedback on their implementation.
|Learning Objectives: At the conclusion of the workshop, participants will be able to:
1) Describe what artificial intelligence and machine learning are
2) Explain the benefits and drawbacks of using machine learning models to solve socially significant problems
3) Describe behavior analytic applications of machine learning
4) Train simple machine learning models to analyze behavior analytic data using Python
|Activities: Workshop objectives will be met through lectures, small group activities, and guided practice.
Supplemental materials will be available online and include a) a description of artificial intelligence and machine learning terms, b) all code and data files to run the analyses presented during the workshop, and c) step-by-step written tutorials on applying machine learning algorithms to behavior analytic data.
It is required that attendees bring a laptop computer to the workshop in order to participate in the guided practice.
|Audience: Advanced graduate students
|Content Area: Methodology
|Instruction Level: Intermediate
|Keyword(s): Artificial intelligence, Data analysis, Machine learning