In this paper we describe a collection of course materials designed to be used in an undergraduate introduction to artificial intelligence (AI) course. These materials include three programming assignments, each touching upon core AI topics, which require that the students build the main functionalities of a guide robot. These assignments were carefully designed to allow the same solution to work both with a robot simulator and an inexpensive web-cam as well as with real robots. An overview of the course and the assignments is given along with references to online versions of the resources developed to teach the course.

Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resource-limited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and computational resources are available, SVM's gender classifiers are superior to the rest. When computational resources are scarce but there is enough data, boosting or linear approaches are adequate. Finally, if training data and computational resources are very scarce, then the linear approach is the best choice.