
Machine Learning with MATLAB
11 Mar 2022   Contributor(s):: Gaby Arellano Bello
In this session, we explore the fundamentals of machine learning using MATLAB. We introduce machine learning techniques available in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results and apply the best technique to your problem.

Data Analysis with MATLAB
04 Mar 2022   Contributor(s):: Gen Sasaki
Learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your coursework as well as in research.

Recursive algorithm for NEGF in Python GPU version
02 Feb 2021   Contributor(s):: Ning Yang, Tong Wu, Jing Guo
This folder contains two Python functions for GPUaccelerated simulation, which implements the recursive algorithm in the nonequilibrium Green’s function (NEGF) formalism. Compared to the matlab implementation [1], the GPU version allows massive parallel running over many cores on GPU...

Simulating Field Theory in the LightFront Formulation
08 Jan 2021   Contributor(s):: Peter J. Love
I will talk about quantum simulation algorithms based on the lightfront formulation of quantum field theory. They will range from ab initio simulations with nearly optimal resource scalings to VQEinspired methods available for existing devices.

Designing a NISQ Reservoir with Maximal Memory Capacity for Volatility Forecasting
28 Oct 2020   Contributor(s):: Samudra Dasgupta
In this talk, we lay out the systematic design considerations for using a NISQ reservoir as a computing engine. We then show how to experimentally evaluate the memory capacity of various reservoir topologies (using IBMQ’s Rochester device) to identify the configuration with maximum...

Samudra Dasgupta
Samudra Dasgupta obtained his B.Tech in Electronics and Electrical Engineering from IITKharagpur 2006, followed by M.S. in Engineering and Applied Sciences from Harvard 2008 and an M.B.A. from...
https://nanohub.org/members/305162

Interactive Learning Tools for Scientific Computing and Data Analysis Using R
29 Jul 2020   Contributor(s):: Cindy Nguyen, Rei SanchezArias
Rootfinding methods and numerical optimization techniques with applications in science, engineering, and data analysis

ECE 595ML Lecture 1.2: Linear Regression  Geometry
28 May 2020   Contributor(s):: Stanley H. Chan

PennyLane  Automatic Differentiation and Machine Learning of Quantum Computations
29 Apr 2020   Contributor(s):: Nathan Killoran
PennyLane is a Pythonbased software framework for optimization and machine learning of quantum and hybrid quantumclassical computations.

Advances in Computational and Quantum Imaging Workshop
28 Jan 2020 
The purpose of the workshop is to bring different communities together, review recent theoretical and experimental advances and explore synergetic collaborations. The workshop aligns well with the significant investments in quantum technologies through the National Quantum Initiative in the...

ECE 595ML Lecture 1.1: Linear Regression
21 Jan 2020   Contributor(s):: Stanley H. Chan

ECE 595ML Lecture 2.1: Regularized Linear Regression
21 Jan 2020   Contributor(s):: Stanley H. Chan

ECE 595ML: Machine Learning I
17 Jan 2020   Contributor(s):: Stanley H. Chan
Spring 2020  This course is in productionCourse Website: https://engineering.purdue.edu/ChanGroup/ECE595/index.htmlCourse Outline:Part 1: Mathematical BackgroundLinear Regression and OptimizationPart 2: ClassificationMethods to train linear classifiersFeature analysis, Geometry, Bayesian...

Universal Variational Quantum Computation
28 Oct 2019   Contributor(s):: Jacob Biamonte
We show that the variational approach to quantum enhanced algorithms admits a universal model of quantum computation.

Quantum Algorithmic Breakeven: on Scaling Up with Noisy Qubits
21 Aug 2019   Contributor(s):: Daniel Lidar
In this talk I will argue in favor of a different criterion I call "quantum algorithmic breakeven," which focuses on demonstrating an algorithmic scaling improvement in an errorcorrected setting over the uncorrected setting. I will present evidence that current experiments with...

Overview of Computational Methods and Machine Learning: Panel Discussion
14 Jun 2019   Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The individual Panel Talks which accompanies this discussion can be found here.Why do we need experiments?Are your methods “descriptive” or “predictive”?Do you work with any other theory/simulation groups?On the 5 year timescale: is machinelearning hype or a real...

Overview of Computational Methods and Machine Learning: Panel Talks
14 Jun 2019   Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The Panel Discussion which follows these individual presentations can be found here.Individucal Presentations:Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie;Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan;Computational Chemistry/Materials, Peilin...

Big Data in Reliability and Security: Some Basics
30 May 2019   Contributor(s):: Saurabh Bagchi

Big Data in Reliability and Security: Applications
30 May 2019   Contributor(s):: Saurabh Bagchi

Peter Shor
Peter Shor is Morss Professor of Applied Mathematics since 2003, and Chair of the Applied Mathematics Committee since 2015. He received the B.A. in mathematics from Caltech in 1981, and the Ph.D....
https://nanohub.org/members/230531