Interested in text analytics and related deep learning? Sai Krishna Theja Bhavaraju a Ph.D. student in Industrial and Systems Engineering will be offering a seminar/tutorial on Monday, February 18, 2019, on Text analytics with Deep Learning. The seminar will be held in the Carson Engineering Center, Room 100 from 3:30 PM to 5:00 PM. The topics include: Introduction… Read More »
DSA Seminar: Data Science frontiers in Oil and Gas Operations Dr. Vikram Jayaram will be speaking on Data Science in Oil and Gas on Friday, November 30, 3-4:30p in Devon Energy Hall 120. Advances in Data Sciences and increases in high throughput computational architectures, cloud-based solutions have driven present-day decision making through the analysis of petabytes… Read More »
One of the areas that I have been intrigued in recently is Artificial Intelligence. In this post, I will discuss one the latest and most fascinating specializations within Artificial Intelligence called Deep Learning. What is Artificial Intelligence (AI)? AI can be understood as the capability of a machine to imitate intelligent human behavior. The term… Read More »
Game Theory and Me “Game Theory”!? If you know what this is about, you are not alone. If you don’t, you are not alone either. When I tell people that my research is about game theory application in road-bridge transportation network. Reaction from most people are that their facial expression tells me they lost interest… Read More »
Non-dominated sorting genetic algorithm II (NSGA-II) In this post, I will attempt to explain some basic ideas of multi-objective optimization and the non-dominated sorting genetic algorithm II (known as NSGA-II to it’s friends). I will also provide an example in Python using the library “inspyred”. But first let’s talk multiple objectives… Multi-objective optimization problems deals… Read More »
In the first of this series of posts on regression techniques we introduced the work-horse of predictive modeling, ordinary least squares regression (OLS), but concluded with the notion that while a common and useful technique, OLS has some notable weaknesses. In the last post we discussed its sensitivity to outliers and how to deal with that using… Read More »
We left off discussion in the last post on regression techniques with the statement that there are some known issues relating to Ordinary Least Squares (OLS) regression techniques. My goal in this series of posts is to introduce several variations on OLS that address one or more of these drawbacks. The first issue I will mention is related… Read More »
I just completed this semester’s series of lectures on regression methods in ISE/DSA 5103 Intelligent Data Analytics and I wanted to take a moment to call out a few key points. First, let me list the primary set of techniques that we covered along with links to the associated methods and package in R: Ordinary… Read More »