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Model Ai Assignments 2018, Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth
Model Ai Assignments 2018, Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth
Computer Science Faculty Publications
The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of seven AI assignments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller
Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller
Computer Science Faculty Publications
In this column, we share resources for learning about and teaching Hidden Markov Models (HMMs). HMMs find many important applications in temporal pattern recognition tasks such as speech/handwriting/gesture recognition and robot localization. In such domains, we may have a finite state machine model with known state transition probabilities, state output probabilities, and state outputs, but lack knowledge of the states generating such outputs. HMMs are useful in framing problems where external sequential evidence is used to derive underlying state information (e.g. intended words and gestures). [excerpt]
Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller
Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller
Computer Science Faculty Publications
In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students. [excerpt]
Playful Ai Education, Todd W. Neller
Playful Ai Education, Todd W. Neller
Computer Science Faculty Publications
In this talk, Neller shared how games can serve as a fun means of teaching not only game-tree search in Artificial Intelligence (AI), but also such diverse topics as constraint satisfaction, logical reasoning, planning, uncertain reasoning, machine learning, and robotics. He observed that teachers teach best when they enjoy what they share and encouraged AI educators present to teach to their unique strengths and enthusiasms.
Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller
Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller
Computer Science Faculty Publications
Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning …