Tag

Machine Learning

Sep 14 2022
1

Dailey receives U-M Robotics’ first-ever alumni award 

By | General Interest, Happenings, News, Research, Uncategorized

Meghan Dailey will be presenting The Future of Machine Learning in Robotics on September 23 at 2 p.m., at FMCRB or Zoom

Meghan Dailey is the U-M Robotics department’s first Alumni Merit Award recipient!

Dailey is a member of the first-ever class in U-M Robotics. She earned a Masters of Science degree in 2015 with a focus in artificial intelligence. She is currently a machine learning specialist with Advanced Research Computing (ARC), a division of Information and Technology Service (ITS). 

You’re invited 

In honor of the award, Dailey will be presenting “The Future of Machine Learning in Robotics” on Friday, September 23, 2 p.m., Ford Robotics Building (FMCRB) or on Zoom (meeting ID: 961 1618 4387, passcode: 643563). Machine learning is becoming widely prevalent in many different fields, including robotics. In a future where robots and humans assist each other in completing tasks, what is the role of machine learning, and how should it evolve to effectively serve both humans and robots? Dailey will discuss her past experiences in robotics and machine learning, and how she envisions machine learning contributing to the growth of the robotics field.

About Dailey

A member of the ARC Scientific Computing and Research Consulting Services team, Dailey helps researchers with machine learning and artificial intelligence programming. She has consulted with student and faculty teams to build neural networks for image analysis and classification. She also has extensive experience in natural language processing and has worked on many projects analyzing text sentiment and intent.

Image courtesy U-M Robotics

Jan 12 2022
0

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

Oct 01 2021
0

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.

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

Sep 15 2021
0

XSEDE: Big Data and Machine Learning

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OVERVIEW

XSEDE, along with the Pittsburgh Supercomputing Center, is pleased to present a two day Big Data and Machine Learning workshop.

This workshop will focus on topics such as Hadoop and Spark and will be presented using the Wide Area Classroom (WAC) training platform.

 

Please see this site for more information

 

Due to COVID-19, this workshop will be remote, using Zoom.

Register by going to: https://portal.xsede.org/xup/course-calendar or 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.

 

Jul 20 2021
0

XSEDE: Big Data and Machine Learning

By |

OVERVIEW

XSEDE, along with the Pittsburgh Supercomputing Center, is pleased to present a two day Big Data and Machine Learning workshop.

This workshop will focus on topics such as Hadoop and Spark and will be presented using the Wide Area Classroom (WAC) training platform.

Due to COVID-19, this workshop will be remote, using Zoom.

Register by going to: https://portal.xsede.org/course-calendar or https://portal.xsede.org/course-calendar/-/training-user/class/2433/session/4094

 

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.

 

Jul 20 2021
0

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.

 

Jul 13 2021
0

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.

Jun 15 2021
0

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.

Jun 10 2021
0

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.

May 14 2021
0

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.