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Hints are sometimes used in online learning system to help students when they are having difficulties. However, in all of the systems we are aware of, the hints are fixed ahead of time and do not depend on the unsuccessful attempts the student has already made. This severely limits the effectiveness of the hints.We have developed an alternative system for giving hints to students. The main difference is that the system allows an instructor to send a hint to a student after the student has made several attempts to solve the problem and failed. After analyzing the student's mistakes, the instructor is better able to understand the problem in the student's thinking and send them a more helpful hint.We have deployed this system in a probability and statistics course with 176 students. We have demonstrated the superiority of the new hints methodology over the traditional one.The limiting factor on the effectiveness of our system is the amount of manual labor required to send each hint. This is the main obstacle we see in scaling this approach to larger classes and to MOOCs. We are currently exploring several approaches for addressing this problem: 1) Letting students send hints to their peers. 2) Creating hint libraries. 3) Using machine learning methods to automate the process of mapping student mistakes to the most relevant hint.

Massive quantities of digital data are being collected in every aspect of modern life. Examples include Personal photos and videos, biological and medical images and recordings from sensor arrays. To transform these massive data streams into useful information we use a sequence of "winnowing" stages. Each step reduces the size of the data by an order of magnitude; extracting the wheat form the chaff. In this talk I will describe this approach in a variety of contexts, ranging from the analysis of genetic pathways in fruit-fly embryos and C-Elegans worms to counting birds and helping elderly people living alone keep in touch with their family and caregivers.

We have built a system that engages naive users in an audio-visual interaction with a computer in an unconstrained public space. We combine audio source localization techniques with face detection algorithms to detect and track the user throughout a large lobby. The sensors we use are an ad-hoc microphone array and a PTZ camera. To engage the user, the PTZ camera turns and points at sounds made by people passing by. From this simple pointing of a camera, the user is made aware that the system has acknowledged their presence. To further engage the user, we develop a face classification method that identifies and then greets previously seen users. The user can interact with the system through a simple hot-spot based gesture interface. To make the user interactions with the system feel natural, we utilize reconfigurable hardware, achieving a visual response time of less than 100ms. We rely heavily on machine learning methods to make our system self-calibrating and adaptive.

We present a simple variant of the k-d tree which automatically adapts to intrinsic low dimensional structure in data without having to explicitly learn this structure.

We propose a new boosting algorithm. This boosting algorithm is an adaptive version of the boost by majority algorithm and combines bounded goals of the boost by majority algorithm with the adaptivity of AdaBoost.

We introduce and analyze a new algorithm for linear classification which combines Rosenblatt‘s perceptron algorithm with Helmbold and Warmuth‘s leave-one-out method. Like Vapnik‘s maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik‘s algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem, while saving significantly on computation time and programming effort.

In this work we present some improvements and extensions to previous work on boosting weak learners [Sch90, Fre90]. Our main result is an improvement of the boosting-by-majority algorithm. One implication of the performance of this algorithm is that if a concept class can be learned in the PAC model to within some fixed error smaller than 1/2, then it can be learned to within an arbitrarily small error e > 0 with time complexity 0((1/e)(log 1/e)2) (fixing the sample space and concept class and the required reliability). We show that the majority rule is the optimal rule for combining general weak learners. We also extend the boosting algorithm to concept classes that give multi-valued labels and real-valued labels.