PPT-Rare Category Detection in Machine Learning

Prafulla Dawadi Topics in Machine Learning Outline Part I Examples Rare Class Imbalanced Class Outliers Part II RareCategory Detection Part III Kernel Density Estimation Mean Shift and Hierarchal Mean Shift. Lecture . 4. Multilayer . Perceptrons. G53MLE | Machine Learning | Dr Guoping Qiu. 1. Limitations of Single Layer Perceptron. Only express linear decision surfaces. G53MLE | Machine Learning | Dr Guoping Qiu. Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. Binarized Normed Gradients for Objectness Estimation at 300fps. Ming-Ming Cheng. 1. Ziming Zhang. 2. Wen-Yan Li. 1. Philip H. S. Torr. 1. 1. Torr . Vision Group, Oxford . University . Stanford University. Learning. . to improve our lives. Input. Computers Can Learn?. Computers can learn to . predict. Computers can learn to . act. Output. Program. Parameters. Learned to get desired input/output mapping. Prafulla Dawadi. Topics in Machine Learning. Outline. Part I. Examples. Rare Class, Imbalanced Class, Outliers. Part II. (Rare)Category Detection. Part III. Kernel Density Estimation . Mean Shift and Hierarchal Mean Shift. What is an IDS?. An . I. ntrusion . D. etection System is a wall of defense to confront the attacks of computer systems on the internet. . The main assumption of the IDS is that the behavior of intruders is different from legal users.. . Thang . Luong. ACL 2015. Joint work with. : . Ilya Sutskever. , . Quoc . Le. , . Oriol Vinyals. , . & . Wojciech. . Zaremba. .. Standard Machine Translation (MT). T. ranslate . locally phrases by phrases: . DistributedattackdetectionschemeusingdeeplearningapproachforInternetofThingsAbebeAbeshuDiro,NaveenChilamkurtiPII:S0167-739X(17)30848-8DOI:http://dx.doi.org/10.1016/j.future.2017.08.043Reference:FUTURE and Their Carriers . Using Compressed Se(. que. ). nsing. Or . Zuk. Broad . Institute of MIT and . Harvard. orzuk@broadinstitute.org. In collaboration with: . Amnon. Amir. Dept. . of Physics of Complex Systems, Weizmann . Presented by Aditi . Kuchi. Supervisor: . Dr.. Md . Tamjidul. Hoque. 1. Presentation Overview. Sand boils – What, How, Why +Motivation. Dataset. Methods used & explanations, discussion. Viola-Jones’ algorithm (. Yonggang Cui. 1. , Zoe N. Gastelum. 2. , Ray Ren. 1. , Michael R. Smith. 2. , . Yuewei. Lin. 1. , Maikael A. Thomas. 2. , . Shinjae. Yoo. 1. , Warren Stern. 1. 1 . Brookhaven National Laboratory, Upton, USA. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity. Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly. Applications (Part I). S. Areibi. School of Engineering. University of Guelph. Introduction. 3. Machine Learning. Types of Learning:. Supervised learning. : (also called inductive learning) Training data includes desired outputs. This is spam this...