Machine Learning for Big Data and Text Processing - Short Programs Testimonials
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MIT Machine Learning for Big Data and Text Processing Class Notes Day 4
On day 4 of the Machine learning course, following was the agenda: Unsupervised learning, clustering Dimensionality reduction, matrix factorization, and Collaborative filtering, recommender problems The day started with Regina Barzilay (Bio) (Personal Webpage) talk on Determining the number of clusters in a data set and approaches to determine the correct numbers of clusters. The core idea being addressed was difference…
MIT Machine Learning for Big Data and Text Processing Class Notes Day 3
Day 3 of the Machine Learning for Big Data and Text Processing Classification started with Dr. Regina Barzilay (Bio) (Personal Webpage) overview of the the following. Cascades, boosting Neural networks, deep learning Back-propagation Image/text annotation, translation Dr. Barzilay introduced BoosTexter for the class with a demo on twitter feed. BoosTexter is a general purpose machine-learning program based on boosting for building…
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…
MIT Machine Learning for Big Data and Text Processing Class Notes - Day 1
As a follow up on MIT's tackling the challenges of Big Data, I am currently in Boston attending Machine Learning for Big Data and Text Processing Classification (and therefore blogging about it for posterity based on public domain data / papers - nothing posted here is MIT proprietary info to violate any T&C). MIT professional education courses are…
Deep Learning with Neural Networks
Deep learning architectures are built using multiple levels of non-linear aggregators, for instance neural nets with many hidden layers. In this introductory talk Will Stanton discusses the motivations and principles regarding learning algorithms for deep architectures. Bill provides a primer to neural networks, and deep Learning. He explains how Deep Learning gives some of the best-ever solutions to problems…
Monads by David Crockford
The monadic curse is that once someone learns what monads are and how to use them, they lose the ability to explain it to other people. Excellent lecture. Transcript and Monads for Humans
Gradient Boosting Machine Learning by Prof. Hastie
Here is Prof. Hastie's recent talk from the H2O World conference. In this talk, professor Hastie takes us through Ensemble Learners like decision trees and random forests for classification problems. Other excellent talks from the conference include the following. Michael Marks - Values and Art of Scale in Business Nachum Shacham of Paypal -…
Selected Papers on Interestingness Measures, Knowledge Discovery and Outlier Mining
S. Abe and T. Inoue. Fuzzy support vector machines for multiclass problems.In ESANN 2002 Proceedings, pages 113-118, 2002. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD Conference, 1993. A. Alink, C. M. Schwiedrzik, A. Kohler, W. Singer, and L.…
Hilary Mason - Machine Learning for Hackers
An interesting beginners talk for machine learning enthusiasts. Ever tried to use a regular expression to parse an unstructured street address? This talk is an introduction to a few machine learning algorithms and some tips for integrating them where they make the most sense and will save you the most headaches. Hilary Mason - Machine…