Adnan Masood, a Machine Learning Phd's musings on Life, Technology, Research & Development
The idea of using of Bayesian Belief Networks in digital forensics to quantify the evidence has been around for a while now. To provide qualitative approaches to Bayesian evidential reasoning in the digital Meta-Forensics is however relatively new in the decision support systems research. For law enforcement, decision support and application of data mining techniques to “soft” forensic evidence is…
I have recently came across Wainwright & Jordan's paper on exponential families, graphical models, and variational inference and found it to be quite comprehensive and unifying introduction of the topic. Probabilistic graphical models use a graph-based representation as the basis for compactly encoding a complex distribution over a high-dimensional space. If you are familiar with Koller and Friedman's work on…
As discussed previous post on Customizing Conditional Probability using Code Generation with SamIam, I have touched upon importance of having programmatic and declarative control over the network. Working with SamIam (and with Infer.NET to some extent) gives a researcher provides this flexibility which is hard to find in proprietary tools. Here is a simple example of a…
While we are having fun visualizing with tag clouds, here is one on the following four key papers in the area of pattern mining with Bayesian networks as background knowledge and discovery of interesting patterns based on Bayesian network background knowledge. Fast discovery of interesting patterns based on Bayesian network background knowledge Fast discovery of…
The Discipline of Machine Learning by Tom Mitchell Introduction to Support Vector Machines - Dustin Boswell Fast Training of Support Vector Machines using Sequential Minimal Optimization Introduction to linear regression The elements of statistical learning (book) Survey of Clustering Algorithms Supervised Machine Learning: A Review of Classification Techniques Ensemble Methods in Machine Learning The Boosting Approach to…
Here is an interesting word cloud built using dissertation by Hei Chan of UCLA on Sensitivity analysis of probabilistic graphical models. Following is the summary of the thesis. Probabilistic belief systems are used in artificial intelligence to model uncertainty. A popular framework for realizing probabilistic belief systems is to use graphical models, such as Bayesian…
"I'm not an outlier; I just haven't found my distribution yet!" -Ronan Conroy Infer.NET is a .NET library for machine learning which provides state-of-the-art algorithms for probabilistic inference from data. Due to its capability of seamlessly integrating with the .NET code, I have used this library frequently and wrote about Infer.Net in the past; however,…
Even though every machine learning practitioner and researcher would like to modify and tweak both the algorithm and parameters, there are limited options for automated code generation in the machine learning world. SamIam by Automated Reasoning Group, UCLA is the tool designed for “Sensitivity Analysis, Modeling, Inference and more”. Currently the sensitivity, MAP, MPE and EM Learning…
Since calling this post just On the history of Bayesian inference will be non sequitur. Judea Pearl won the ACM Turing Award, widely considered the "Nobel Prize in Computing" for his contribution in the areas of developing "Novel Framework for Reasoning under Uncertainty that Changed How Scientists Approach Real World Problems". This framework is the notion of Bayesian Networks aka Bayesian Belief Networks…
Discovering meaningful, explainable and interesting rules from mined data has been a challenging research problem due to its highly subjective nature. It is deemed as the central task in knowledge data and discovery to explore and analyze relationship between features. For most practical applications, it is quite difficult to determine the most relevant rule for…