Notes from Microsoft Machine Learning and Data Science Summit - Day 2

Microsoft Machine Learning and Data Science Summit promised to connect Big Data engineers, Data Scientists, Machine Learning practitioners and managers to share best practices, to Dive deep & learn, and to facilitate Think big & execute fast ideas. In my experience, the summit met majority of its goals and it was a great start. Microsoft has graciously made majority...

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Exploring Spark with Data Science Work bench

Apache Spark is a general purpose cluster computing platform which extends map-reduce to support multiple computation types including but not limited to stream processing and interactive queries. Last week IBM's Moktar Kandil presented at the Tampa Hadoop and Tampa Data Science Group Joint meetup on the topic of exploring Apache Spark. Apache Spark for Azure HD-Insight Following are...

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The five Tribes of Machine Learning, and other algorithmic tales

Pedro Domingos' The Master Algorithm - How the Quest for the Ultimate Learning Machine Will Remake Our World is an interesting and thought provoking book about the state of machine learning, data science, and artificial intelligence.   Categorizing,  classifying and clearly representing the ideas around any rapidly developing/evolving field is hard job. Machine learning with its...

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On Explainability of Deep Neural Networks

During a discussion yesterday with software architect extraordinaire David Lazar regarding how everything old is new again, the topic of deep neural networks and its amazing success was brought up. Unless one is living under a rock for past five years, the advancements in artificial neural networks (ANN) has been quite significant and noteworthy. Since the...

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MIT Machine Learning for Big Data and Text Processing Class Notes Day 5

On the final day (day 5) the agenda for the MIT Machine learning course was as follows: Generative models, mixtures, EM algorithm Semi-supervised and active learning Tagging, information extraction The day started with Dr. Jakkola's discusion on parameter selection, generative learning algorithms,  Learning Generative Models via Discriminative Approaches, and Generative and Discriminative Models. This led to the questions such...

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MIT Machine Learning for Big Data and Text Processing Class Notes - Day 2

So after having an awesome Day 1 @ MIT, I was in CSAIL library and met Pedro Ortega, NIPS 2015 Program Manager @adaptiveagents. Celebrity sighting! Today on Day 2, Dr. Jaakkola (Bio) (Personal Webpage) professor, Electrical Engineering and Computer Science/Computer Science and Artificial Intelligence Laboratory (CSAIL), went over the following . Non-linear classification and regression, kernels Passive aggressive algorithm Overfitting, regularization, generalization Content...

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