Demystifying Machine Learning - PDF Free Download

Demystifying Machine Learning Dmitry Figol, WW Enterprise Sales Systems Engineer - Programmability @dmfigol CTHRST-1002

More examples YouTube, Spotify, Amazon recommender systems Speech recognition: Siri, Google Home, Alexa IBM Watson Google ranking results Facebook s facial recognition Many more CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 10

What is Machine Learning Machine learning is a collection of algorithms to make computers learn from and make decisions and predictions based on data CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 11

ML is not a silver bullet Dirty, missing data Not enough data Hard to convert unstructured data to mathematical values Hard to find a good model Do results make sense? Is it bug or expected result? Incorrect model? Data scientists are needed CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 12

Data in Machine Learning It is all about data Data should be cleaned Bad data leads to bad results Big data is required to get good results CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 14

Types of Machine Learning

Supervised Learning Solves classification and regression problems based on labeled training data Classification: assign groups to input data based on previous data Regression: predicts real values to input data based on previous data CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 17

Google recaptcha behind the scenes Uses Machine Learning to identify if your behavior is human If the behavior is suspicious, it shows a 3x3 grid of images (for example, street signs) Some of them Google knows are street signs Some of them Google thinks are street signs Some of them Google knows are not street signs Your choice helps to label images for Google self-driving cars CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 19

Data in Supervised learning Each data entry has features (x 1, x 2,.., x n ) and a label y Features represent different data characteristics, a label outcome/result Training data set contains records where a label is known Data is fed into the algorithm and the model is computed Model represents correlation of features to a label Machine can now predict labels for new feature values CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 20

Regression example A big number of network engineers filled in a survey with questions about their skills, experience, number of employees in their company, region and salary Skills, years of experience, information about company, region are features Salary is a label A model describing correlation of features to a label is found based on the survey data Now we can predict the salary for a person who didn t take a survey CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 21

Linear Regression The simplest supervised learning algorithm Linear relation between features and labels Intuition: find a model so that an error between all training labels and predicted labels is minimal Salary Salary vs experience Experience CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 22

Unsupervised Machine Learning Solves clustering and association problems based on unlabeled training data Clustering: discovering grouping in the data Association: finding rules about data CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 23

Reinforcement Learning Based on exploring the world Rewards for actions are known Start with random actions, record rewards with every action Build a policy by preferring actions that lead to higher rewards Continue improving the policy with every experience Examples: both AlphaGo and OpenAI played with themselves thousands of times until they learned strategies that are close to perfect in complex games CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 24

Deep Learning State of the art in Machine Learning Mostly for supervised learning Uses neural networks (the first application: to model brain neurons) More complex Requires more data Requires more time to train CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 25

Deep Learning applications Speech and image recognition Natural language processing Machine translation (DeepL) Self-driving cars Many more CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 26

Machine Learning in Networking

The Network. Intuitive. Constantly learning, adapting and protecting. DNA Center Policy Automation Analytics L E A R N I N G Informed by Context Visibility into traffic and threat patterns Who, What, When, Where, How Powered by Intent I N T E N T Translate Business Intent to Network Policy Automate the management and provisioning millions of devices instantly C O N T E X T Intent-based Network Infrastructure S E C U R I T Y CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 29

Encrypted Traffic Analytics (ETA) Known malware traffic Known benign traffic Extract observable features in the data Employ machine learning techniques to build detectors Known malware sessions detected in encrypted traffic with 99% accuracy Identifying encrypted malware traffic with contextual flow data AISec 16 Blake Anderson, David McGrew (Cisco Fellow) CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 30

How it works Initial Data Packet Make the most of the unencrypted fields Sequence of Packet Lengths and Times Identify the content type through the size and timing of packets Threat Intelligence Map Who s who of the Internet s dark side Data exfiltration Self-Signed certificate C2 message Broad behavioral information about the servers on the Internet. CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 31

Malware detection with ETA Initial Data Packet Cloud-based machine learning Threat Intelligence Map Sequence of Packet Lengths and Times All three elements reinforce each other inside the analytics engine using them. CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 32

DNA Center Analytics Distributed Stream Processing Continuous processing, aggregating, correlating and analyzing data in motion Distributed analytics pipeline runtime and programming model Real-time or near real-time Analytics Operations: Time Machine Learning Time Series Series Analysis Analysis Complex Complex Event Event Processing Processing Machine Learning CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 33

Get Started with Machine Learning Machine Learning by Andrew Ng online course Coursera/Stanford Reinforcement Learning course by David Silver CS231n Convolutional Neural Networks for Visual Recognition - Stanford CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 34

Cisco Webex Teams Questions? Use Cisco Webex Teams (formerly Cisco Spark) to chat with the speaker after the session How 1 2 3 4 Find this session in the Cisco Events App Click Join the Discussion Install Webex Teams or go directly to the team space Enter messages/questions in the team space Webex Teams will be moderated by the speaker until June 18, 2018. cs.co/ciscolivebot#cthrst-1002 CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 35

Complete your online session evaluation Give us your feedback to be entered into a Daily Survey Drawing. Complete your session surveys through the Cisco Live mobile app or on www.ciscolive.com/us. Don t forget: Cisco Live sessions will be available for viewing on demand after the event at www.ciscolive.com/online. CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 36

Continue your education Demos in the Cisco campus Walk-in self-paced labs Meet the engineer 1:1 meetings Related sessions CTHRST-1002 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 37

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