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Analytics-CRM Community Town Hall Machine Learning & Automated Modeling March 18, 2015 We will be starting at the top of the hour. Please stay on mute.

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Presentation on theme: "Analytics-CRM Community Town Hall Machine Learning & Automated Modeling March 18, 2015 We will be starting at the top of the hour. Please stay on mute."— Presentation transcript:

1 Analytics-CRM Community Town Hall Machine Learning & Automated Modeling March 18, 2015 We will be starting at the top of the hour. Please stay on mute *6 -- not hold. This session is NOT being recorded.

2 This is Your Member Community Members are always welcome to: » Share knowledge with other analytics pros. » Contribute to industry thought leadership. » Get published or be a discussion leader. » Network to advance your business and career. » with your questions or

3 »Monthly Calls »Analytics Journal »Analytics Challenge 2015 – details soon »Analytics Advantage – be a guest blogger »Awareness – Advertise or sponsor »Self-Regulation – be compliant Analytics-CRM Community »Industry Visibility – be a speaker or discussion facilitator »Thought Leader – assist in DMA content planning »Members access all at

4 Upcoming Events Register at Apr 8Big Data, Small Data, Clean Data, Messy Data Stephen Yu, Willow Data Strategy May 13Town Hall Jun10Big Data Enabled Analytics for Actionable Customer Insight Amit Deshpande, Epsilon July 8Town Hall Aug 12Practical Text AnalyticsSteven Struhl, Converge Analytic

5 Today’s Town Hall Topic Machine Learning & Automated Modeling Led by: »Marty Rose, Senior Data Scientist, Acxiom »Peter Zajonc, Senior Director, Epsilon Please stay on mute *6, not hold, until prompted for questions or use chat function to post questions. This session is NOT being recorded.

6 edelmachine.html Goedel machines are self- referential universal problem solvers making provably optimal self- improvements. Where To Next?

7 Definitions – What it is Machine Learning Automated modeling Big Data

8 Differences How is Automated modeling different in the context of Machine Learning and Big Data? What are the benefits and why should we pay attention to this?

9 Automated Analytic Techniques »Data Reduction and Standardization »Predictive Modeling Traditional: Simple to Complex Regression Statistical Learning Techniques »Unsupervised Classification Techniques

10 Too much data incoming?!

11 Two Real life examples 1.Recommender System – Beauty Products at Retail Counter 2.New Data Product Extensions

12 Letting go is hard…(!)

13 Future What’s the role of the future analyst in this brave new world? -or- Do the analysts still hold the keys to the car? Who will be driving?

14 References – Will be posted on web site Useful intro videos »Machine Learning: Real Basics, with Ron Bekkerman (LinkedIn Tech Talks) -- https://www.youtube.com/watch?v=wjTJVhmu1JM https://www.youtube.com/watch?v=wjTJVhmu1JM »Lecture 01 - The Learning Problem (CalTech) -- https://www.youtube.com/watch?v=mbyG85GZ0PIhttps://www.youtube.com/watch?v=mbyG85GZ0PI »Lecture 1 | Machine Learning for Engineers (Stanford) -- https://www.youtube.com/watch?v=UzxYlbK2c7Ehttps://www.youtube.com/watch?v=UzxYlbK2c7E »For do-it-yourselfers (Amazon Web Services and Python) -- https://www.youtube.com/watch?v=k890Dr5OkZg&list=PLRJx8WOUx5XdosSIpI34ijGVAxCSG_jjT https://www.youtube.com/watch?v=k890Dr5OkZg&list=PLRJx8WOUx5XdosSIpI34ijGVAxCSG_jjT Critique and Articles »http://www.kdnuggets.com/2015/03/all-machine-learning-models-have-flaws.htmlhttp://www.kdnuggets.com/2015/03/all-machine-learning-models-have-flaws.html »http://www.kdnuggets.com/2015/03/machine-learning-data-science-common-mistakes.htmlhttp://www.kdnuggets.com/2015/03/machine-learning-data-science-common-mistakes.html »http://ml.posthaven.com/machine-learning-done-wronghttp://ml.posthaven.com/machine-learning-done-wrong »Forrester Wave Reports. Big Data Streaming Analytics, Web Analytics, Cross-Channel Business Analytics, Big Data Hadoop Solutions, etc. Gartner. Magic Quadrant Reports, which classifies participating companies into four quadrants: visionaries, leaders, challengers, and niche players. Marty’s Book List »An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie & Tibshirani – This book is fantastic and has helped me quite a bit. It provides an overview of several methods, along with the R code for how to complete them. 426 Pages.An Introduction to Statistical Learning with Applications in R »The Elements of Statistical Learning by Hastie, Tibshirani & Friedman – This is an in-depth overview of methods, complete with theory, derivations & code. I’d definitely consider this a graduate level text. I’d also consider it one of the best books available on the topic of data mining. 745 Pages.The Elements of Statistical Learning »A Programmer’s Guide to Data Mining by Ron Zacharski – This one is an online book, each chapter downloadable as a PDF. It’s also still in progress, with chapters being added a few times each year.A Programmer’s Guide to Data Mining

15 References (continued) »Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilson – This book is absolutely fantastic. The author explains Bayesian statistics, provides several diverse examples of how to apply and includes Python code. Each chapter is an iPython notebook that can be downloaded.Probabilistic Programming & Bayesian Methods for Hackers »Think Bayes, Bayesian Statistics Made Simple by Allen B. Downey – Another great, easy to digest introduction to Bayesian statistics. The author’s premise is that Bayesian statistics is easier to learn & apply within the context of reusable code samples. It includes a number of examples complete with Python code. 195 Pages.Think Bayes, Bayesian Statistics Made Simple »Data Mining and Analysis, Fundamental Concepts and Algorithms by Zaki & Meira – This title is new to me. It’s a text book that looks to be a complete introduction with derivations & plenty of sample problems. 599 Pages.Data Mining and Analysis, Fundamental Concepts and Algorithms »An Introduction to Data Science by Jeffrey Stanton – Overview of the skills required to succeed in data science, with a focus on the tools available within R. It has sections on interacting with the Twitter API from within R, text mining, plotting, regression as well as more complicated data mining techniques. 195 Pages.An Introduction to Data Science »Machine Learning by Chebira, Mellouk & others – This is an introduction to more advanced machine learning methods. It includes chapters on neural networks, discriminant analysis, natural language processing, regression trees & more, complete with derivations. Each chapter is downloadable as a PDF. 422 Pages.Machine Learning »Machine Learning – The Complete Guide – This one is new to me. It’s a collection of Wikipedia articles organized into chapters & downloadable in a number of formats. I didn’t realize they did this, but its a great idea. Because its a collection of individual articles, it covers quite a bit more material than a single author could write. Bayesian Reasoning and Machine Learning by David Barber – This is an undergraduate textbook. It includes an overview, derivations, sample problems and MATLAB code. 648 Pages.Machine Learning – The Complete Guide Bayesian Reasoning and Machine Learning »A Course in Machine Learning by Hal Daumé III – Another complete introduction to machine learning topics. Each chapter is individually downloadable. 189 Pages.A Course in Machine Learning »Information Theory, Inference and Learning Algorithms by David J.C. MacKay – Nice overview of machine learning topics, including an introduction and derivations. One nice feature of this book is that it has a chart that shows how various topics are related to one another. 628 Pages.Information Theory, Inference and Learning Algorithms »Advanced R by Hadley Wickham. To get the most out of this book, you’ll need to have written a decent amount of code in R or another programming language.Advanced R

16 Your turn Questions | Comments Use the chat function or toggle *6 to unmute/mute yourself. For free flow discussion, this session is NOT being recorded.

17 Speakers Contact Info Peter Zajonc, Epsilon Martin Rose, Acxiom

18 Continue the conversation Linked In: Official DMA + the Analytics Community Group #dmacommunities Suggest topics: Participate in the planning of projects Connect with more Communities: THANK YOU FOR ATTENDING


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