Computer games are a multi-billion dollar industry and have become an important part of our private and social lives. It is only natural, then, that the technology used to create games should become part of a computing science curriculum. However, game development is more than a massive programming endeavor. Today's games are largely about generating content within multidisciplinary teams. CMPUT 250 is a new computing science course at the University of Alberta that emphasizes creating games in multidisciplinary teams. This paper describes our experiences with the course, emphasizing the issues of multidisciplinary interactions: teaching, teamwork, and evaluation.

We present a new unsupervised algorithm for training structured predictors that is discriminative, convex, and avoids the use of EM. The idea is to formulate an unsupervised version of structured learning methods, such as maximum margin Markov networks, that can be trained via semidefinite programming. The result is a discriminative training criterion for structured predictors (like hidden Markov models) that remains unsupervised and does not create local minima. To reduce training cost, we reformulate the training procedure to mitigate the dependence on semidefinite programming, and finally propose a heuristic procedure that avoids semidefinite programming entirely. Experimental results show that the convex discriminative procedure can produce better conditional models than conventional Baum-Welch (EM) training.

Images and other high-dimensional data can frequently be characterized by a low dimensional manifold (e.g. one that corresponds to the degrees of freedom of the camera). Recently, nonlinear manifold learning techniques have been used to map images to points in a lower dimension space, capturing some of the dynamics of the camera or the subjects. In general, these methods do not take advantage of any prior understanding of the dynamics we might have, relying instead on local Euclidean distances that can be misleading in image space. In practice, we frequently have some prior knowledge regarding the transformations that relate images (e.g. rotation, translation, etc). We present a method for augmenting existing embedding techniques with additional information derived from known transformations, either in the form of tangent spaces that locally characterize the manifold or distances derived from reconstruction errors. The extra information is incorporated directly into the cost function of the embedding technique. The techniques we augment are largely attractive because there is a closed form solution for their cost optimization. Our approach likewise produces a closed form solution for the augmented cost function. Experiments demonstrate the effectiveness of the approach on a variety of image data.

Greedy importance sampling is an unbiased estimation technique that re- duces the variance of standard importance sampling by explicitly search- ing for modes in the estimation objective. Previous work has demon- strated the feasibility of implementing this method and proved that the technique is unbiased in both discrete and continuous domains. In this paper we present a reformulation of greedy importance sampling that eliminates the free parameters from the original estimator, and introduces a new regularization strategy that further reduces variance without com- promising unbiasedness. The resulting estimator is shown to be effective for difficult estimation problems arising in Markov random field infer- ence. In particular, improvements are achieved over standard MCMC estimators when the distribution has multiple peaked modes.