7CCSMBDT – Big Data Technologies Week 1 Grigorios Loukides, PhD (grigorios.loukides@kcl.ac.uk) Spring 2017/2018 1 Today Logistics Brief overview of the module Introduction to the topic Big Data Technologies Data analytics & Big data 2 Logistics Practicals (from Week 2 onwards): Monday, 9-11, Bush House (S) 7.01/2/3 […]
Introducing Symbolic Execution Slide deck courtesy of Prof. Michael Hicks, University of Maryland, College Park (UMD) Software has bugs • Software has bugs To find them, we use testing and code reviews • • Software has bugs To find them, we use testing and code reviews But some bugs are still missed • • •
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7CCSMSEN: Security Engineering Application Security Pt3 Lorenzo Cavallaro http://s2lab.kcl.ac.uk Systems Security Research Lab – Cybersecurity Research Group Department of Informatics, King’s College London Lorenzo Cavallaro (S2 Lab) 7CCSMSEN 1 / 55 Part I Solutions Lorenzo Cavallaro (S2 Lab) 7CCSMSEN 2 / 55 Solutions to Buffer Overflows Prevent Write decent programs! (impossible) Use a language that
7CCSMSEN: Security Engineering Application Security Pt2 Lorenzo Cavallaro http://s2lab.kcl.ac.uk Systems Security Research Lab – Cybersecurity Research Group Department of Informatics, King’s College London Lorenzo Cavallaro (S2 Lab) 7CCSMSEN 1 / 43 Part I Stack-based overflows and shellcode Lorenzo Cavallaro (S2 Lab) 7CCSMSEN 2 / 43 Stack Overflow Data is copied without checking boundaries Data “overflows”
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7CCSMBDT – Big Data Technologies Week 11 Grigorios Loukides, PhD (grigorios.loukides@kcl.ac.uk) 1 Objectives Introduce the format of the exam Go through the main concepts quickly Answer questions 2 Exam The exam will have a weight 80% (the rest 20% from the two courseworks) Format We are waiting for formal
COMP9444 Neural Networks and Deep Learning Outline COMP9444 ⃝c Alan Blair, 2017-20 COMP9444 ⃝c Alan Blair, 2017-20 COMP9444 20T2 Image Processing 2 COMP9444 20T2 Image Processing 3 4b. Image Processing Image Datasets and Tasks AlexNet Data Augmentation (7.4) Weight Initialization (8.4) Batch Normalization (8.7.1) Residual Networks Textbook, Sections 7.4,
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COMP9444 Neural Networks and Deep Learning Outline COMP9444 ⃝c Alan Blair, 2017-20 COMP9444 ⃝c Alan Blair, 2017-20 COMP9444 20T2 Hidden Unit Dynamics 2 COMP9444 20T2 Hidden Unit Dynamics 3 3a. Hidden Unit Dynamics geometry of hidden unit activations (8.2) limitations of 2-layer networks vanishing/exploding gradients alternative activation functions (6.3) Textbook, Sections
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A Reconstruction-Free Projection Selection Procedure for Binary Tomography Using Convolutional Neural Networks Gergely Pap1, Ga ́bor L ́ek ́o2(B), and Tama ́s Gr ́osz1 1 Department of Computer Algorithms and Artificial Intelligence, University of Szeged, A ́rpa ́d t ́er 2, Szeged 6720, Hungary {papg,groszt}@inf.u-szeged.hu 2 Department of Image Processing and Computer Graphics, University of
Name of Candidate: …………………………………………….. Student id: …………………………………………….. Signature: …………………………………………….. THE UNIVERSITY OF NEW SOUTH WALES Term 2, 2020 COMP9417 Machine Learning and Data Mining – Sample Final Examination (SOLUTIONS) 1. I ACKNOWLEDGE THAT ALL OF THE WORK I SUBMIT FOR THIS EXAM WILL BE COMPLETED BY ME WITHOUT ASSISTANCE FROM ANYONE ELSE. 2. TIME ALLOWED
Unsupervised Learning COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Unsupervised Learning Term 2, 2020 1 / 91 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book