Anomaly Detection : Time Series Talk
Detecting anomalies and adjusting for them in time series. Code used in this video: https://github.com/ritvikmath/Time-Series-Analysis/blob/master/Anomaly%20Detection.ipynb
From playlist Time Series Analysis
2 Sample t Test v Paired t Test
Identifying the difference between situations when a 2-sample t Test is appropriate and when a paired t Test is appropriate, including the recognition of paired dependent data versus independent samples.
From playlist Unit 9: t Inference and 2-Sample Inference
Modern Anomaly and Novelty Detection: Anomaly Detection - Session 2
Anomaly detection approaches Anomaly detection techniques Deep learning based approaches
From playlist Modern Anomaly and Novelty Detection
The alternating series. Test for convergence.
From playlist Advanced Calculus / Multivariable Calculus
Testing alternating series for convergence/divergence and computing error bounds
From playlist 242 spring 2012 exam 3
Anomaly Detection for JavaScript Apps
Watch this video to learn about the anomaly detection tools which will enable you to monitor and detect abnormalities in your JavaScript apps. PUBLICATION PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com
From playlist JavaScript
Machine Learning for Cyber Security - Session 8
anomaly detection anonmalous events with categorical typically no metric space for comparison how to learn normal vs abnormal , esp with no labels, self-supervised embeddings NCE
From playlist Machine Learning for Cyber Security
Testing for absolute convergence.
From playlist Advanced Calculus / Multivariable Calculus
Watch Everything, Watch Anything: Anomaly Detection By Nathaniel Cook
Five years ago Ian Malpass posted his “Measure Anything, Measure Everything” article that introduced StatsD to the world. Since then DevOps has grown and defined itself around the ideal to measure everything. Now it’s time to take it further. “Watch Everything, Watch Anything”. At any Dev
From playlist DevOpsDays Salt Lake City 2016
RailsConf 2021: Scaling Rails API to Write-Heavy Traffic - Takumasa Ochi
Tens of millions of people can download and play the same game thanks to mobile app distribution platforms. Highly scalable and reliable API backends are critical to offering a good game experience. Notable characteristics here is not only the magnitude of the traffic but also the ratio of
From playlist RailsConf 2021
Data-Driven Anomaly Detection | Nikunj Oza | Talks at Google
This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defin
From playlist NASA Speakers at Google
Dynamic Meta-Learning for Anomaly Detection: Cole Sodja, Microsoft Defender ATP
This talk will propose a methodology for measuring probabilistic calibration and updating scores dynamically and conditionally incorporating this feedback by learning adaptive mixtures of functional inflated beta-binomial models. An application for identifying and updating scores for cyber
From playlist Microsoft Defender: At scale anomaly detection for enterprise cyber defence Open configuration options
Cuneyt Akçora: (10/7/20): TDA on Networks – Applications and Scalability issues
Title: Topological Data Analysis on Networks – Applications and Scalability issues Abstract: Over the last couple of years, Topological Data Analysis (TDA) has seen a growing interest from Data Scientists of diverse backgrounds. TDA is an emerging field at the interface of algebraic topol
From playlist AATRN 2020
Machine Learning with scikit learn Part Two | SciPy 2017 Tutorial | Andreas Mueller & Alexandre Gram
Tutorial materials found here: https://scipy2017.scipy.org/ehome/220975/493423/ Machine learning is the task of extracting knowledge from data, often with the goal of generalizing to new and unseen data. Applications of machine learning now touch nearly every aspect of everyday life, fro
From playlist talks
At Scale Anomaly Detection for Enterprise Security: Joshua Neil, Microsoft
In this talk, Joshua will present a modular, scalable system for streaming anomaly detection for enterprise cyber security, along with some real user stories of such detections. Microsoft Defender Advanced Threat Protection is a suite of tools for enterprise defense. In particular, the E
From playlist Microsoft Defender: At scale anomaly detection for enterprise cyber defence Open configuration options
Machine Learning without the Hype
What is artificial intelligence, machine learning, and deep learning mean in general? When is a rule-based approach the right solution and when do you need machine learning? What does machine learning mean for time-series data? What is the difference between supervised and unsupervised lea
From playlist Talks
Some Forays in BSM Physics by Sudhir Vempati
DISCUSSION MEETING PARTICLE PHYSICS: PHENOMENA, PUZZLES, PROMISES ORGANIZERS: Amol Dighe, Rick S Gupta, Sreerup Raychaudhuri and Tuhin S Roy, Department of Theoretical Physics, TIFR, India DATE & TIME: 21 November 2022 to 23 November 2022 VENUE: Ramanujan Lecture Hall and Online While t
From playlist Particle Physics: Phenomena, Puzzles, Promises - (Edited)
Modern Anomaly and Novelty Detection: Exercise - Session 7
Anomaly visualization Outlier threshold Q&A Using standard deviation Assumptions of the data
From playlist Modern Anomaly and Novelty Detection
DEFCON 13: Auto-adapting Stealth Communication Channels
Speaker: Daniel Burroughs, University of Central Florida Intrusion detection systems and firewalls generally follow one of two methods of attack detection, signature or anomaly. Signature detection detects known attacks and anomaly detection covers unusual activity (with the hope that i
From playlist DEFCON 13