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

XSEDE HPC Workshop: GPU Programming Using OpenAcc

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OVERVIEW

XSEDE, along with the Pittsburgh Supercomputing Center is pleased to present an OpenACC GPU programming workshop.

OpenACC is the accepted standard using compiler directives to allow quick development of GPU capable codes using standard languages and compilers. It has been used with great success to accelerate real applications within very short development periods. This workshop assumes knowledge of either C or Fortran programming. It will have a hands-on component using the Bridges-2 computing platform at the Pittsburgh Supercomputing Center.

Due to Covid-19, this workshop will delivered via zoom.

More information can be found at https://www.psc.edu/resources/training/xsede-hpc-workshop-march-2-2021-gpu-programming-using-openacc-2/

Deadline for registration is  2 PM February 26

Registration with XSEDE

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

By |

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

Choosing Machine Learning Tools and Training

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OVERVIEW

With many languages, packages, and applications available to researchers, many wonder how to choose the correct path forward when starting a machine learning project. In this workshop, we will discuss the differences between several machine learning packages and applications that are best suited for each available language. A review of basic machine learning concepts, as well as a basic grasp of Python, is recommended.

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

Introduction to Machine Learning

By |

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

ARC-TS joins Cloud Native Computing Foundation

By | General Interest, Happenings, News

Advanced Research Computing – Technology Services (ARC-TS) at the University of Michigan has become the first U.S. academic institution to join the Cloud Native Computing Foundation (CNCF), a foundation that advances the development and use of cloud native applications and services. Founded in 2015, CNCF is part of the Linux Foundation.

CNCF announced ARC-TS’s membership at the KubeCon and CloudNativeCon event in Copenhagen. A video of the opening remarks by CNCF Executive Director Dan Kohn can be viewed on the event website.

“Our membership in the CNCF signals our commitment to bringing cloud computing and containers technology to researchers across campus,” said Brock Palen, Director of ARC-TS. “Kubernetes and other CNCF platforms are becoming crucial tools for advanced machine learning, pipelining, and other research methods. We also look forward to bring an academic perspective to the foundation.”

ARC-TS’s membership and participation in the group signals its adoption and commitment to cloud-native technologies and practices. Users of containers and other CNCF services will have access to experts in the field.

Membership gives the U-M research community input into in the continuing development of cloud-native applications, and within CNCF-managed and ancillary projects. U-M is the second academic institution to join the foundation, and the only one in the U.S.

U-M fosters thriving artificial intelligence and machine learning research

By | General Interest, HPC, News, Research

Research using machine learning and artificial intelligence — tools that allow computers to learn about and predict outcomes from massive datasets — has been booming at the University of Michigan. The potential societal benefits being explored on campus are numerous, from on-demand transportation systems to self-driving vehicles to individualized medical treatments to improved battery capabilities.

The ability of computers and machines generally to learn from their environments is having transformative effects on a host of industries — including finance, healthcare, manufacturing, and transportation — and could have an economic impact of $15 trillion globally according to one estimate.

But as these methods become more accurate and refined, and as the datasets needed become bigger and bigger, keeping up with the latest developments and identifying and securing the necessary resources — whether that means computing power, data storage services, or software development — can be complicated and time-consuming. And that’s not to mention complying with privacy regulations when medical data is involved.

“Machine learning tools have gotten a lot better in the last 10 years,” said Matthew Johnson-Roberson, Assistant Professor of Engineering in the Department of Naval Architecture & Marine Engineering and the Department of Electrical Engineering and Computer Science. “The field is changing now at such a rapid pace compared to what it used to be. It takes a lot of time and energy to stay current.”

Diagram representing the knowledge graph of an artificial intelligence system, courtesy of Jason Mars, assistant professor, Electrical Engineering and Computer Science, U-M

Johnson-Roberson’s research is focused on getting computers and robots to better recognize and adapt to the world, whether in driverless cars or deep-sea mapping robots.

“The goal in general is to enable robots to operate in more challenging environments with high levels of reliability,” he said.

Johnson-Roberson said that U-M has many of the crucial ingredients for success in this area — a deep pool of talented researchers across many disciplines ready to collaborate, flexible and personalized support, and the availability of computing resources that can handle storing the large datasets and heavy computing load necessary for machine learning.

“The people is one of the reasons I came here,” he said. “There’s a broad and diverse set of talented researchers across the university, and I can interface with lots of other domains, whether it’s the environment, health care, transportation or energy.”

“Access to high powered computing is critical for the computing-intensive tasks, and being able to leverage that is important,” he continued. “One of the challenges is the data. A major driver in machine learning is data, and as the datasets get more and more voluminous, so does the storage needs.”

Yuekai Sun, an assistant professor in the Statistics Department, develops algorithms and other computational tools to help researchers analyze large datasets, for example, in natural language processing. He agreed that being able to work with scientists from many different disciplines is crucial to his research.

“I certainly find the size of Michigan and the inherent diversity that comes with it very stimulating,” he said. “Having people around who are actually working in these application areas helps guide the direction and the questions that you ask.”

Sun is also working on analyzing the potential discriminatory effects of algorithms used in decisions like whether to give someone a loan or to grant prisoners parole.

“If you use machine learning, how do you hold an algorithm or the people who apply it accountable? What does it mean for an algorithm to be fair?” he said. “Can you check whether a particular notion of non-discrimination is satisfied?”

Jason Mars, an assistant professor in the Electrical Engineering and Computer Science department and co-founder of a successful spinoff called Clinc, is applying artificial intelligence to driverless car technology and a mobile banking app that has been adopted by several large financial institutions. The app, called Finie, provides a much more conversational interface between users and their financial information than other apps in the field.

“There is going to be an expansion of the number of problems solved and number of contributions that are AI-based,” Mars said. He predicted that more researchers at U-M will begin exploring AI and ML as they understand the potential.

“It’s going to require having the right partner, the right experts, the right infrastructure, and the best practices of how to use them,” he said.

He added that U-M does a “phenomenal job” in supporting researchers conducting AI and ML research.

“The level of support and service is awesome here,” he said. “Not to mention that the infrastructure is state of the art. We stay relevant to the best techniques and practices out there.”

Advanced Research Computing at U-M, in part through resources from the university-wide Data Science Initiative, provides computing infrastructure, consulting expertise, and support for interdisciplinary research projects to help scientists conducting artificial intelligence and machine learning research.

For example, Consulting for Statistics, Computing and Analytics Research, an ARC unit, has several consultants on staff with expertise in machine learning and predictive analysis with large, complex, and heterogeneous data. CSCAR recently expanded capacity to support very large-scale machine learning using tools such as Google’s TensorFlow.

CSCAR consultants are available by appointment or on a drop-in basis, free of charge. See cscar.research.umich.edu or email cscar@umich.edu for more information.

CSCAR also provides workshops on topics in machine learning and other areas of data science, including sessions on Machine Learning in Python, and an upcoming workshop in March titled “Machine Learning, Concepts and Applications.”

The computing resources available to machine learning and artificial intelligence researchers are significant and diverse. Along with the campus-wide high performance computing cluster known as Flux, the recently announced Big Data cluster Cavium ThunderX will give researchers a powerful new platform for hosting artificial intelligence and machine learning work. Both clusters are provided by Advanced Research Computing – Technology Services (ARC-TS).

All allocations on ARC-TS clusters include access to software packages that support AI/ML research, including TensorFlow, Torch, and Spark ML, among others.

ARC-TS also operates the Yottabyte Research Cloud (YBRC), a customizable computing platform that recently gained the capacity to host and analyze data governed by the HIPAA federal privacy law.

Also, the Michigan Institute for Data Science (MIDAS) (also a unit of ARC) has supported several AI/ML projects through its Challenge Initiative program, which has awarded more than $10 million in research support since 2015.

For example, the Analytics for Learners as People project is using sensor-based machine learning tools to translate data on academic performance, social media, and survey data into attributes that will form student profiles. Those profiles will help link academic performance and mental health with the personal attributes of students, including values, beliefs, interests, behaviors, background, and emotional state.

Another example is the Reinventing Public Urban Transportation and Mobility project, which is using predictive models based on machine learning to develop on-demand, multi-modal transportation systems for urban areas.

In addition, MIDAS supports student groups involved in this type of research such as the Michigan Student Artificial Intelligence Lab (MSAIL) and the Michigan Data Science Team (MDST).

(A version of this piece appeared in the University Record.)