Fundamentals of AI for STEM Learners


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This is a 15-hour hybrid course. Learners will meet synchronously via Zoom and will have asynchronous activities. Learners will have eight weeks upon registration to complete the five modules which will require the students to complete at least three hours of work each week. At the end of the course, students will earn a badge for course completion. Learners will earn 1.5 continuing education units (CEUs).

By the end of this course, students will be able to:

  • Describe Statistical and Neural algorithms for AI, including
  • Multi-layer Perceptrons (MLPs)
  • Dimensionality Reduction
  • Support Vector Machines (SVMs)
  • Random Forests
  • Gibbs Samplers
  • Topic Models
  • Convolutional Neural Networks (CNNs)
  • Recursive Neural Networks (RNNs)
  • Autoencoders
  • Generative Adversarial Networks
  • Use Jupyter Notebook implementations of Statistical and Neural algorithms

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