Adnan Masood, a Machine Learning Phd's musings on Life, Technology, Research & Development
Decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. In this concise yet informative article Dr. Takashi J Ozaki outlines decision boundaries for deep learning and other Machine Learning classifiers and emphasize on parameter tuning for Deep Learning. The source code for this article is on github, and he…
Last week I attended Zeyuan Allen-Zhu dissertation defense on the topic of Novel Frameworks for Auctions and Optimization. The abstract of the talk follows. Abstract: This thesis introduces novel frameworks for modeling uncertainty in auctions, and for understanding first-order methods in optimization. The former provides robust analysis to alternative specifications of preferences and information structures in Vickrey…
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…
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…
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…
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…
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 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…
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 -…
Over a decade ago, Peter Flach of Bristol University wrote a paper on the topic of "On the state of the art in machine learning: A personal review" in which he reviewed several, then recent books, related to developments in machine learning. This included Pat Langley’s Elements of Machine Learning (Morgan Kaufmann), Tom Mitchell’s Machine Learning (McGraw-Hill), and…