Reinforcement Learning Dynamic Programming; Monte Carlo Methods Subramanian Ramamoorthy School of Informa=cs 27 January 2017 Recap: Key Quan–es defining an MDP • System dynamics are stochas-c – represented by a probability distribu-on. • Problem is defined as maximiza-on of expected rewards – Recall that E(X) = Σ xi p(xi) for finite-state systems 27/01/2017 Reinforcement Learning […]
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Introduction to information system Model Evaluation Metrics Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science MASH • Maths • And • Stats • Help • MASH • mash@lincoln.ac.uk • In The Library mailto:mash@lincoln.ac.uk • What Is A Model Evaluation Metric? • Mean Absolute Error (MAE) • Root Mean Squared Error (RMSE)
CO2017 All candidates Midsummer Examinations 2015 DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR Department Computer Science Module Code CO2017 Module Title Operating Systems, Networks, and Distributed Systems Exam Duration Three hours CHECK YOU HAVE THE CORRECT QUESTION PAPER Number of Pages 7 Number of Questions 6 Instructions
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COMP3421 COMP3421 Vector geometry, Clipping Transformations • We specify objects in model co-ordinates • Transform them into world co-ordinates • Transform the world into eye/camera- coordinates • We represent our vertices/points as 1D Matrices in homogeneous co-ordinates • We multiply by matrices to transform them Homogenous coordinates We can use a single notation to describe
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CS233 Lab 2 Handout “Beware of bugs in the above code; I have only proved it correct, not tried it.” – Donald E. Knuth Learning Objectives 1. Combinational logic design. 2. Using bitwise logical and shifting operations in a high-level language like C++. Work that needs to be handed in These files need to be
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CS124 Lecture 5 Spring 2011 Minimum Spanning Trees A tree is an undirected graph which is connected and acyclic. It is easy to show that if graph G(V,E) that satisfies any two of the following properties also satisfies the third, and is therefore a tree: • G(V,E) is connected • G(V,E) is acyclic • |E|
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CS233 Lab 13 Handout “It’s hardware that makes a machine fast. It’s software that makes a fast machine slow.” – Craig Bruce Learning Objectives Performance optimization and cache conscious programing, including 1. Analysis of cache access patterns 2. Loop tiling / strip mining 3. Single pass vs. multi-pass algorithms 4. Software prefetch insertion Work that
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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang∗ Xinyu Zhou∗ Mengxiao Lin Jian Sun Megvii Inc (Face++) {zhangxiangyu,zxy,linmengxiao,sunjian}@megvii.com Abstract We introduce an extremely computation efficient CNN architecture named Shuf- fleNet, designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two proposed operations, pointwise
Microsoft Word – Assignment3v.4.docx Latent Variables and Neural Networks Part A. Document Clustering Question 1 I. Derive Expectation and Maximisation steps of the hard-EM algorithm for Document Clustering, show your work in your submitted report. In particular, include all model parameters that should be learnt and the exact expressionthat should be used to update these
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Reinforcement Learning Some Remarks reg. Material for Exam Subramanian Ramamoorthy School of Informa
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