A few years after this post appeared, a paper appeared on the same topic, analyzing 4 different Bitcoin bubbles. On…
SVM+ / LUPI: Learning Using Privileged Information
Recently Facebook hired Vapnik, the father of the Support Vector Machine (SVM) and the Vapnik-Chrevoniks Statistical Learning Theory. Lesser known, Vapnik…
Machine Learning with Missing Labels Part 3: Experiments
In this series of posts we look at Transductive and SemiSupervised Learning–an old problem, a hard problem, and a fundamental problem Machine Learning. Unlike Deep…
Machine Learning with Missing Labels Part 2: The UniverSVM
Ever wonder what Google DeepMind is up to? They just released a paper on Semi-Supervised learning with Deep Generative Models. What is Semi…
Machine Learning with Missing Labels: Transductive SVMs
SVMs are great for building text classifiers–if you have a set of very high quality, labeled documents. Frequently, we just…
Data Science Leadership
My talk from Berkeley is up on the new Calculation Consulting YouTube channel https://www.youtube.com/channel/UCaao8GHAVcRtSZdpObC4_Kg
Foundations: The Partition Function.
We are going to examine the Partition function that arises in Deep Learning methods like Restricted Boltzmann Machines. We take…
Music Recommendations and the Logistic Metric Embedding
In this post, we are going to see how to build our own music recommender, using the Logistic Metric Embedding…
A Ruby DSL Design Pattern for Distributed Computing
Frequently in my work in big data and machine learning, I need to run large calculations in parallel. There are…
Causality, Correlation, and Brownian Motion
A recent question on Quora asked if machine learning could learn something from the Black Scholes model of Finance http://www.quora.com/Machine-Learning/Can-we-learn-any-lessons-from-the-Black-Scholes-solution-to-pricing-risk-to-machine-learning-algorithms-for-personalization-recommendation-algorithms…
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