Tag

Machine Learning

Machine Learning on Great Lakes

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OVERVIEW

This workshop will go over methods and best practices for running machine learning applications on Great Lakes. We will briefly outline machine learning before stepping through a hands-on example problem to load a project and submit a job to the HPC cluster. Participants are expected to be familiar with Python, the command line, and basic Great Lakes functionality (logging in and navigating the directory structure). Participants must create a user account on Great Lakes prior to the workshop and are required to pre-register to gain access to a training account.

INSTRUCTOR:

Meghan Dailey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing

Meghan Dailey is a machine learning specialist in the Advanced Research Computing (ARC) department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 1:00-2:00 PM for computer setup assistance.

Please note, this session will be recorded.  

To register and view more details, please refer to the linked TTC page.

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu

Machine Learning on Great Lakes

By |

OVERVIEW

This workshop will go over methods and best practices for running machine learning applications on Great Lakes. We will briefly outline machine learning before stepping through a hands-on example problem to load a project and submit a job to the HPC cluster. Participants are expected to be familiar with Python, the command line, and basic Great Lakes functionality (logging in and navigating the directory structure). Participants must create a user account on Great Lakes prior to the workshop and are required to pre-register to gain access to a training account.

INSTRUCTORS

Meghan Richey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing

Meghan Richey is a machine learning specialist in the Advanced Research Computing (ARC) department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 1:00-2:00 PM for computer setup assistance.

Please note, this session will be recorded.  

To register and view more details, please refer to the linked TTC page.

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu

XSEDE: Python Tools for Data Science

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OVERVIEW

Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2 .

See https://portal.xsede.org/course-calendar/-/training-user/class/2467/session/4161 for more information and registration

 

Register via the XSEDE Portal:

If you do not currently have an XSEDE Portal account, you will need to create one:

https://portal.xsede.org/my-xsede?p_p_id=58&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_58_struts_action=%2Flogin%2Fcreate_account

Should you have any problems with that process, please contact help@xsede.org and they will provide assistance.

 

Geostatistics – III

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this third workshop, we will combine the material we covered in the first two workshops and develop the geostatistical modeling approach. This is mainly a lecture style workshop, but will include an example in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.

Geostatistics – II

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we are covering the basics of Geostatistics. In this second workshop, we will focus on covariance and variogram, and their estimation in the context of geostatistical modeling. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.

Geostatistics – I

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sparsely sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this first workshop we will understand the idea of stationary random fields, positive definite functions, and the fundamental building blocks of Gaussian random fields. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the foundations of Gaussian Process Regression, a commonly used technique in Machine Learning and AI.

XSEDE HPC HPC Summer Boot Camp

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OVERVIEW

XSEDE, along with the Pittsburgh Supercomputing Center is pleased to present a Hybrid Computing workshop.

This 4 day event will include MPI, OpenMP, GPU programming using OpenACC and accelerators.

This workshop will be remote to desktop only due to the COVID-19 pandemic.  When the registration has filled, there will be no more students added due to our current limits.

The schedule can be found here:  https://www.psc.edu/resources/training/xsede-hpc-workshop-june-8-11-2021-summer-boot-camp/

 

Register via the XSEDE Portal:

https://portal.xsede.org/course-calendar/-/training-user/class/2338/session/4002

If you do not currently have an XSEDE Portal account, you will need to create one:

https://portal.xsede.org/my-xsede?p_p_id=58&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_58_struts_action=%2Flogin%2Fcreate_account

Should you have any problems with that process, please contact help@xsede.org and they will provide assistance.

Questions

Please address any questions to Tom Maiden at tmaiden@psc.edu.

Advanced ML topics: Algorithms, writing ML code, comparing implementations

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OVERVIEW

This workshop is designed as a follow-up to the basic introduction to machine learning earlier in this series. We will cover several examples in Python and compare different implementations. We will also look at advanced topics in machine learning, such as GPU optimization, parallel processing, and deep learning. A basic understanding of Python is required.

INSTRUCTORS

Meghan Richey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing – Technology Services

Meghan Richey is a machine learning specialist in the Advanced Research Computing- Technology Services department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 9-10 AM for computer setup assistance.

Please note, this session will be recorded.  

Register here

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu

Introduction to Machine Learning

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OVERVIEW

Machine learning is becoming an increasingly popular tool in several fields, including data science, medicine, engineering, and business. This workshop will cover basic concepts related to machine learning, including definitions of basic terms, sample applications, and methods for deciding whether your project is a good fit for machine learning. No prior knowledge or coding experience is required

INSTRUCTORS

Meghan Richey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing – Technology Services

Meghan Richey is a machine learning specialist in the Advanced Research Computing- Technology Services department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

MATERIALS

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 9-10 AM for computer setup assistance.

Please note, this session will be recorded.  

Register here

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu

Balzano wins NSF CAREER award for research on machine learning and big data involving physical, biological and social phenomena

By | General Interest, Happenings, News, Research

Prof. Laura Balzano received an NSF CAREER award to support research that aims to improve the use of machine learning in big data problems involving elaborate physical, biological, and social phenomena. The project, called “Robust, Interpretable, and Efficient Unsupervised Learning with K-set Clustering,” is expected to have broad applicability in data science.

Modern machine learning techniques aim to design models and algorithms that allow computers to learn efficiently from vast amounts of previously unexplored data, says Balzano. Typically the data is broken down in one of two ways. Dimensionality-reduction uses an algorithm to break down high-dimensional data into low-dimensional structure that is most relevant to the problem being solved. Clustering, on the other hand, attempts to group pieces of data into meaningful clusters of information.

However, explains Balzano, “as increasingly higher-dimensional data are collected about progressively more elaborate physical, biological, and social phenomena, algorithms that aim at both dimensionality reduction and clustering are often highly applicable, yet hard to find.”

Balzano plans to develop techniques that combine the two key approaches used in machine learning to decipher data, while being applicable to data that is considered “messy.” Messy data is data that has missing elements, may be somewhat corrupted, or is filled heterogeneous information – in other words, it describes most data sets in today’s world.

Balzano is an affiliated faculty member of both the Michigan Institute for Data Science (MIDAS) and the Michigan Institute for Computational Discovery and Engineering (MICDE). She is part of a MIDAS-supported research team working on single-cell genomic data analysis.

Read more about the NSF CAREER award…