The Association for Computing Machinery is hosting a Town Hall with Peter Norvig on A.I., Machine Learning, and More. Peter Norvig is the Research Director at Google, and a leader in the field of Artificial Intelligence (AI). Topics he might discuss:
- deep learning
- future of AI
- teaching AI
- academia vs industry
- advice for grad students
The free webinar is Thursday, December 08, 2016 at noon Eastern time.
This is just a short list of a few books that I have have recently discovered online.
- Model-Based Machine Learning – Chapters of this book become available as they are being written. It introduces machine learning via case studies instead of just focusing on the algorithms.
- Foundations of Data Science – This is a much more academic-focused book which could be used at the undergraduate or graduate level. It covers many of the topics one would expect: machine learning, streaming, clustering and more.
- Deep Learning Book – This book was previously available only in HTML form and not complete. Now, it is free and downloadable.
R is a hugely popular language among data scientists and statisticians. One of the difficulties with open-source R is the memory constraint. All the data needs to be loaded into a data.frame. Microsoft solves this problem with the RevoScaleR package of the Microsoft R Server. Just launched this week is an EdX course on
Analyzing Big Data with Microsoft R Server.
According the syllabus:
Upon completion, you will know how to use R for big-data problems.
Full Disclosure: I work at Microsoft, and the course instructor, Seth Mottaghinejad, is one of my colleagues.
Our World in Data is data visualization site for exploring the history of civilization. The site was created by Max Roser. Our World in Data contains tons of information about many aspects of people’s lives. It also includes numerous visuals (like the one below) which can be easily shared or embedded on other sites.
Beware, the site is addicting, and you might spend a lot of time exploring data.
Somewhat lost in the hype of Google’s Cloud Machine Learning announcement (which is itself neat), was the release of Google’s Public Data Sets.
I think this has been previously happening, but now Google has an official location for these public data sets stored in BigQuery. You can:
- Access and use the data in your applications
- Request Google to host your own public data set
It will be fun to watch this site expand with more public datasets. Happy Exploration!
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Associate Professor at the School of Computer
Science and Engineering at The Hebrew University, Israel, and
Shai Ben-David, Professor in the School of Computer Science at the
University of Waterloo, Canada. The book looks very thorough. Below is just a sampling of the topics covered.
- Bias-Complexity Tradeoff
- Model Selection
- Support Vector Machines
- Decision Trees
- Neural Networks
- Dimensionality Reduction
- Feature Selection and Generation
- Advanced Theory
- And LOTS LOTS more….
O’Reilly just published a free ebook profiling 15 influential women in data science, Women in Data. The book is written by Cornelia Levy-Bencheton.
The following women are profiled in the book:
- Michele Chambers, COO of RapidMiner
- Camille Fournier, CTO of Rent the Runway
- Carla Gentry, CEO of Analytical Solution
- Kelly Hoey, Speaker and Early-stage Investor
- Cindi Howson, VP of Research at Gartner
- Neha Narkhede, Co-founder of Confluent
- Claudia Perlich, Chief Scientist at Dstillery
- Kira Radinsky, Co-founder of SalesPredict
- Gwen Shapira, Software Engineer at Cloudera
- Laurie Skelly, Data Scientist at Datascope
- Kathleen Ting, Technical Account Manager at Cloudera
- Renetta Garrison Tull, Associate Vice Provost of UMBC
- Hanna Wallach, Researcher at Microsoft
- Alice Zheng, Director of Data Science at Dato
- Margit Zwemer, Founder of LiquidLandscape
DataQuest is a recently launched online data science learning platform for python. The site consists of a gamified series of missions that increase in difficulty as your skills progress. Here are a few other features of the site.
- Sample Code
- Live, Interactive Browser-based Coding Environment
- Step by Step Instructions
- Instant Feedback
- Helpful Forums for Q&A
The site is still under development and the founder, Vik Paruchuri, is looking for help developing more content and missions for the site. If that is something of interest to you, get in touch with Vik via the DataQuest website.
The creators is the Insight Data Science Fellows Program have done it again. This time they have created the Insight Data Engineering Program. The program aims to training highly specialized software engineers that can build big data systems and big data pipelines. Unlike the data science program, the data engineering program does not target people with PhDs. Please visit the Insight Data Engineering website for a white paper with all the details on the program.
Here is an official announcement:
The Insight Data Engineering Fellows Program is a professional training fellowship designed to help engineers from various backgrounds, as well as mathematicians, and computer scientists, transition to careers in data engineering. – Tuition free, 6 week, full-time, data engineering training fellowship in Silicon Valley this summer. – Alumni network of 70 Insight Fellows who are now data scientists and data engineers at Facebook, LinkedIn, Microsoft, Twitter, Square, Netflix, Airbnb, Palantir, Jawbone and many others. – Interview at top technology companies hiring data engineers at the end of the fellowship. For more information please visit: