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Course Description

Intro to AI in Agriculture and Life Sciences 1 Hour Course Description & Objectives:

  • This course is asynchronous, meaning you can sign up any time throughout the year.
  •  Artificial intelligence (AI) is used to solve problems in research and industry. This course provides students with an introduction to the concepts and techniques used to build and using AI systems. Students will obtain the knowledge they need to understand how AI is used to solve real-world agricultural and life sciences problems.
    •  By the end of this course, students will be able to:
      •  Demonstrate a basic understanding of modern AI and the history of AI development.
      •  Identify emerging applications of AI in the agricultural and life sciences fields.

Survey of AI in Agriculture 4 Hour Course Description and Objectives:

  • This course is asynchronous, meaning you can sign up any time throughout the year.
  • Artificial intelligence (AI) is used to solve problems in research and industry. This course provides students with an introduction to the concepts and tools used to build and using AI systems. Students will obtain the skills and knowledge they need to understand how AI is used to solve real-world agricultural and life sciences problems.
  • After successfully completing this course, you will earn 0.4 CEU as well as a certificate of completion.
    • By the end of this course, students will be able to:
      • Use Google Colaboratory (Google Colab) and Jupyter Notebooks to build and train neural networks.
      • Demonstrate a basic understanding of modern AI and the history of AI development, using correct vocabulary to describe the characteristics of neural networks.
      • Implement neural networks in TensorFlow.
      • Identify important applications of phenotype prediction in agricultural and life sciences.

Notes

A badge or certificate is not awarded for the 1 hour course. 
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Enroll Now - Select a section to enroll in
Section Title
Intro AI in Agriculture and Life Science- 1 hour
Type
self-paced
Dates
Start Now, you have 60 days to complete this course once enrolled.
Course Fee(s)
1 hour non-credit $0.00
Drop Request Deadline
TBD
Transfer Request Deadline
TBD
Section Notes
Description & Objectives:
  •  This course is asynchronous, meaning you can sign up any time throughout the year.
  •  Artificial intelligence (AI) is used to solve problems in research and industry. This course provides students with an introduction to the concepts and techniques used to build and using AI systems. Students will obtain the knowledge they need to understand how AI is used to solve real-world agricultural and life sciences problems.
    •  By the end of this course, students will be able to:
      •  Demonstrate a basic understanding of modern AI and the history of AI development.
      •  Identify emerging applications of AI in the agricultural and life sciences fields.
Section Title
Survey of AI in Agriculture- 4 hour
Type
self-paced
Dates
Start Now, you have 180 days to complete this course once enrolled.
Course Fee(s)
Registration fee non-credit $149.00
Drop Request Deadline
TBD
Transfer Request Deadline
TBD
Section Notes
Description & Objectives:
  •  This course is asynchronous, meaning you can sign up any time throughout the year.
  •  Artificial intelligence (AI) is used to solve problems in research and industry. This course provides students with an introduction to the concepts and tools used to build and using AI systems. Students will obtain the skills and knowledge they need to understand how AI is used to solve real-world agricultural and life sciences problems.
  •  After successfully completing this course, you will earn .4 CEU as well as a certificate of completion.
    •  By the end of this course, students will be able to:
      •  Use Google Colaboratory (Google Colab) and Jupyter Notebooks to build and train neural networks.
      •  Demonstrate a basic understanding of modern AI and the history of AI development, using correct vocabulary to describe the characteristics of neural networks.
      •  Implement neural networks in TensorFlow.
      •  Identify important applications of phenotype prediction in agricultural and life sciences.
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