AI Education: Birds of a Feather

Games are beautifully crafted microworlds that invite players to explore complex terrains that spring into existence from even simple rules. As AI educators, games can offer fun ways of teaching important concepts and techniques. Just as Martin Gardner employed games and puzzles to engage both amateurs and professionals in the pursuit of Mathematics, a well-chosen game or puzzle can provide a catalyst for AI learning and research. [excerpt

AI Education: Deep Neural Network Learning Resources

In this column, we focus on resources for learning and teaching deep neural network learning. Many exciting advances have been made in this area of late, and so many resources have become available online that the flood of relevant concepts and techniques can be overwhelming. Here, we hope to provide a sampling of high-quality resources to guide the newcomer into this booming field. [excerpt

Pedagogical Possibilities for the 2048 Puzzle Game

In this paper, we describe an engaging puzzle game called 2048 and outline a variety of exercises that can leverage the game’s popularity to engage student interest, reinforce core CS concepts, and excite student curiosity towards undergraduate research. Exercises range in difficulty from CS1-level exercises suitable for exercising and assessing 1D and 2D array skills to empirical undergraduate research in Monte Carlo Tree Search methods and skilled heuristic evaluation design

AI Education: Machine Learning Resources

In this column, we focus on resources for learning and teaching three broad categories of machine learning (ML): supervised, unsupervised, and reinforcement learning. In ournext column, we will focus specifically on deep neural network learning resources, so if you have any resource recommendations, please email them to the address above. [excerpt

AI Education Matters: Teaching Hidden Markov Models

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

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

AI Education Matters: Data Science and Machine Learning with Magic: The Gathering

In this column, we briefly describe a rich dataset with many opportunities for interesting data science and machine learning assignments and research projects, we take up a simple question, and we offer code illustrating use of the dataset in pursuit of answers to the question

AI Education: Open-Access Educational Resources on AI

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 experiences for each topic from scratch. In this column, we focus on a few online resources that we would recommend to AI Educators looking to find good starting points for course development. [excerpt

Pigtail: A Pig Addendum

The object of the jeopardy dice game Pig is to be the first player to reach 100 points. Each turn, a player repeatedly rolls a die until either a 1 is rolled or the player holds and scores the sum of the rolls (i.e., the turn total). At any time during a player’s turn, the player is faced with two choices: roll or hold. If the player rolls a 1, the player scores nothing and it becomes the opponent’s turn. If the player rolls a number other than 1, the number is added to the player’s turn total and the player’s turn continues. If the player instead chooses to hold, the turn total is added to the player’s score and it becomes the opponent’s turn. In our original article [Neller and Presser 2004], we described a means to compute optimal play for Pig. Since that time, we have also solved a number of Pig variants. In this addendum, we review the optimality equations for Pig, show how these equations change for several Pig variants, and show how the resulting optimal policies change accordingly. [excerpt

Practical Play of the Dice Game Pig

The object of the jeopardy dice game Pig is to be the first player to reach 100 points. Each turn, a player repeatedly rolls a die until either a 1 is rolled or the player holds and scores the sum of the rolls (i.e., the turn total). At any time during a player’s turn, the player is faced with two choices: roll or hold. If the player rolls a 1, the player scores nothing and it becomes the opponent’s turn. If the player rolls a number other than 1, the number is added to the player’s turn total and the player’s turn continues. If the player instead chooses to hold, the turn total is added to the player’s score and it becomes the opponent’s turn. In our original article [Neller and Presser 2004], we described a means to compute optimal play for Pig. However, optimal play is surprisingly complex and beyond human potential to memorize and apply. In this paper, we mathematically explore a more subjective question: What is the simplest human-playable policy that most closely approximates optimal play? While one cannot enumerate and search the space of all possible simple policies for Pig play, our exploration will present interesting insights and yield a surprisingly good policy that one can play by memorizing only three integers and using simple mental arithmetic. [excerpt