The course begins with a brief history of Artificial Intelligence (AI), including a survey of representative AI success stories and covers topics such as AI data requirements and conditioning, a selection of AI techniques including supervised learning, unsupervised learning and reinforcement learning, applications including computer vision and natural language processing, and computing and hardware requirements to support AI and Big Data applications.
In addition, the course covers properties and techniques that lead to robust AI solutions and review effective human-machine teaming principles and requirements. Each of the AI system components is addressed at a level deep enough to provide a working knowledge of the key technical drivers. Through it all, the course emphasizes an AI system architecture approach applied to engineering prototypes. We highlight strengths and weaknesses of AI solutions and illustrate the role AI can play in augmenting human intelligence.
The course content is presented via video. This course is not for credit and is not graded, but we have added knowledge checks that you can use if you want to review your understanding of the content. We have also included surveys to help us refine the course, they are voluntary but we appreciate any feedback you can share with us.
