What you'll learn


This is a technical crash course in Automated Decision (Augmentation) Systems with a focus on bringing non-technical consultants, risk, and policy professionals up to speed on these emerging technologies. The goal is to gain a sufficient understanding of modern techniques to perform risk analysis and governance.


This is part of a larger series of courses by The Algorithmic Bias Lab, the research and education division of BABL AI. The lab conducts research and training in algorithmic auditing and the responsible production and governance of artificial intelligence. You can find sample lectures from previous training sessions on our Youtube channel.


Who is this for?


  • People working on the ethics and governance of AI and emerging technologies, or those looking to transition into the field 
  • People that need to interface between technical teams and executives or senior management
  • People who lack a deep technical background in algorithms, AI, and machine learning
  • People that feel like a deeper understanding of these technologies is needed to further their career


What can you do after taking this course?


  • List and understand the most common techniques used in AI and machine learning
  • Understand the methods, data, and resources needed to create machine learning and statistical models for automated decision systems (ADMs)
  • Identify critical value judgments that must be made in the development of ADMs
  • Communicate effectively and confidently with development teams and executive decision makers
  • Use Python to create basic algorithms without fear



What will you be doing?


  • 18 lectures (asynchronous) 
  • 4 synchronous Q&A sessions with the instructor
  • 12 short quizzes 
  • 4 coding projects (in Python)
  • 10-15 hours effort per week for approximately 5 weeks
  • Dedicated Slack workspace for student collaboration/networking
  • Certificate of completion is provided with 70% or greater score




About your instructor


Shea Brown is the founder and CEO of BABL AI, a research consultancy that focuses on the ethical use and development of artificial intelligence. His research addresses algorithm auditing and bias in machine learning, and he serves as a ForHumanity Fellow that sets standards for the organizational governance of artificial intelligence. He has a PhD in Astrophysics from the University of Minnesota and is currently a faculty member in the Department of Physics & Astronomy at the University of Iowa, where he has been recognized for his teaching excellence from the College of Liberal Arts & Sciences.

Curriculum


  1: Introduction & Overview
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  2: Conceptual Overview
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  3: Classic Algorithms
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  4: Handcrafted Knowledge & Artificial Intelligence
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  5: Classification Problems
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  6: Linear Networks
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  7: Deep Neural Networks
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  8: Reinforcement Learning
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  9: Natural Language Processing
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  10: Computer Vision
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  11: Miscellaneous
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