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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…

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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…

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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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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…

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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 -…

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A Deep Dive into Causality with Judea Pearl

For most researchers in the ever growing fields of probabilistic graphical models, belief networks, causal influence and probabilistic inference, ACM Turing award winner Dr. Judea Pearl and his seminary papers on causality are well-known and acknowledged. Representation and determination of Causality, the relationship between an event (the cause) and a second event (the effect), where…

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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.…

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