Deep Learning Summer School, Montreal 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. If that is you, there are plenty of videos to help you learn more.
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.
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….