This interactive course will teach network security professionals machine learning techniques and applications for network data. This course is a continuation of the skills taught in the Crash Course in Data Science for Hackers. Students will learn various machine learning methods, applications, model selection, testing, and interpretation. Participants will write code to prepare and explore their data and then apply machine learning methods for discovery.

Jair Aguirre is a life-long tinkerer and has created and hacked everything from custom computers to hot rod engines to music. He is currently a Lead Data Scientist at Booz Allen Hamilton and has over 17 years experience supporting insight discovery and analytics for multiple clients and organizations. Jair's professional passion lies in bringing advanced methods to the mainstream, innovating automated discovery techniques, and prediction for technology risk and opportunity. Jair holds a Master of Science Degree in Applied Economics from The Johns Hopkins University, a Bachelor of Science Degree in Liberal Studies from Excelsior College, and holds certifications in CEH, CPT, Security, Network, EMC Data Science, Hortonworks HDP, and IIF Forecasting Practice. Charles Givre is an unapologetic data geek who is passionate about helping others learn about data science and become passionate about it themselves.

Our First meetup will be focused on H2O, an open-source Machine Learning Platform. It will take place the CIC on Milk Street in Boston.. the Date is set for 5/12 at 6:00PM. NOTE: Bring Laptops if you want to partake in hands on demo. This workshop will provide an overview of how to use H2O, the scalable open source machine learning library, from Python/R/Flow UI. The core algorithms of H2O are implemented in Java, however, fully-featured APIs are available in R, Python, Scala, and also through the Flow UI web interface.

This is an introductory tutorial on using Theano, the Python library. I'm going to start from scratch and assume no previous knowledge of Theano. However, understanding how neural networks work will be useful when getting to the code examples towards the end. I recently gave this tutorial as a talk in University of Cambridge and it turned out to be way more popular than expected. In order to give more people access to the material, I'm now writing it up as a blog post. I do not claim to know everything about Theano, and I constantly learn new things myself.

Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning. In this post you will discover the Support Vector Machine (SVM) machine learning algorithm. SVM is an exciting algorithm and the concepts are relatively simple. This post was written for developers with little or no background in statistics and linear algebra.

Here is the link to coursera course for NLP Pick the software from the The Stanford NLP (Natural Language Processing) Group and input some text to view its parse tree, named entities, part of speech tags, etc.

"Any sufficiently advanced technology is indistinguishable from magic" – Arthur Clarke In the space of technology, this quote has never been so true. Daily discoveries are not uncommon, with yearly revolutions seldom missed. Machine learning has recently been exploding in popularity, with everyone rushing to see how it can benefit their lives, but how did we get to this stage of innovation? This discovery process of machine learning is what you will learn from this article. I was surprised by how much I learned researching machine learning's upbringing, there is enough drama and action to write a novel about it.

This paper discusses the Turing Test as an educational activity for undergraduate students. It describes in detail an experiment that we conducted in a first-year non-CS course. We also suggest other pedagogical purposes that the Turing Test could serve.

Recent years have seen increasing interest in AI from outside the AI community. This is partly due to applications based on AI that have been used in real-world domains, for example, the successful deployment of game theory-based decision aids in security domains. This paper describes our teaching approach for introducing the AI concepts underlying security games to diverse audiences. We adapted a game-based research platform that served as a testbed for recent research advances in computational game theory into a set of interactive role-playing games. We guided learners in playing these games as part of our teaching strategy, which also included didactic instruction and interactive exercises on broader AI topics. We describe our experience in applying this teaching approach to diverse audiences, including students of an urban public high school, university undergraduates, and security domain experts who protect wildlife. We evaluate our approach based on results from the games and participant surveys.

In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer to this task as “knowledge translation” (KT). Unlike data translation and transfer learning, KT does not require any data from the source or target schema. We adopt a probabilistic approach to KT by representing the knowledge in the source schema, the mapping between the source and target schemas, and the resulting knowledge in the target schema all as probability distributions, specially using Markov random fields and Markov logic networks. Given the source knowledge and mappings, we use standard learning and inference algorithms for probabilistic graphical models to find an explicit probability distribution in the target schema that minimizes the Kullback-Leibler divergence from the implicit distribution. This gives us a compact probabilistic model that represents knowledge from the source schema as well as possible, respecting the uncertainty in both the source knowledge and the mapping. In experiments on both propositional and relational domains, we find that the knowledge obtained by KT is comparable to other approaches that require data, demonstrating that knowledge can be reused without data.