Transactions on Neural Networks, Vol. Jure Leskovec, Kevin J. Proceedings of the 19th international conference on World wide web.
Unfortunately, easy-to-use tools for creating such pedagogical resources are not available to the educators, resulting in most courses being taught using a disconnected set of static materials, which is not only ineffective for learning AI, but further, requires repeated and redundant effort for the instructor.
In this paper, we introduce Moro, a software tool for easily creating and presenting AI-friendly teaching materials. Moro notebooks integrate content of different types text, math, code, imagesallow real-time interactions via modifiable and executable code blocks, and are viewable in browsers both as long-form pages and as presentations.
Creating notebooks is easy and intuitive; the creation tool is also in-browser, is WYSIWYG for quick iterations of editing, and supports a variety of shortcuts and customizations for efficiency. We present three deployed case studies of Moro that widely differ from each other, demonstrating its utility in a variety of scenarios such as in-class teaching and conference tutorials.
Tulio RibeiroS. Explaining the Predictions of Any Classifier.Congcong Li, Guangda Su, Yan Shang, Yingchun Li, Yan Xiang.
“Face Recognition Based on Pose-Variant Image Synthesis and Multi-level Multi-feature Fusion.” In Workshop on Analysis and Modeling of Faces and Gestures, International Conference on Computer Vision. Tree Boosting With XGBoost Why Does XGBoost Win "Every" Machine Learning Competition?
(Chen and Guestrin, ), and while it is conceptually similar to Friedmans tree boosting method MART, it also diﬀers in multiple ways. In this thesis, we will determine how .
In the thesis, the author describes the CART because MART uses CART, XGBoost also implements a Tree model related to CART.
Chen, T. and Guestrin, C. ().
Xgboost: A scalable tree boosting. Efﬁcient Methods for Multi-Objective Decision-Theoretic Planning Diederik M. Roijers Institute for Informatics University of Amsterdam The Netherlands. Carlos Guestrin, Milos Hauskrecht and Branislav Kveton; In the Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada, July [ PDF version ].
Solving Factored POMDPs with Linear Value Functions, Carlos Guestrin, Daphne Koller and Ronald Parr, In the IJCAI workshop on Planning under Uncertainty and Incomplete Information. Max-norm Projections for Factored MDPs, Carlos Guestrin, Daphne Koller, and Ronald Parr, AAAI Spring Symposium, Stanford, California, March