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  • Author: karenjoh

    The Michigan Tech College of Computing offers a full range of undergraduate and graduate degrees in the Computing disciplines.

    New Course: Applied Machine Learning


    Summary

    • Course Number: 84859, EET 4996-01
    • Class Times: T/R, 9:30-10:45 am
    • Location: EERC 0723
    • Instructor: Dr. Sidike Paheding
    • Course Levels: Graduate, Undergraduate
    • Prerequisite: Python Programming and basic knowledge of statistics.
    • Preferred knowledge: Artificial Intelligence (CS 4811) or Data Mining (CS4821) or Intro to Data Sciences (UN 5550)

    Course Description/Overview

    Rapid growth and remarkable success of machine learning can be witnessed by tremendous advances in technology, contributing to the fields of healthcare, finance, agriculture, energy, education, transportation and more. This course will emphasize on intuition and real-world applications of Machine Learning (ML) rather than statistics behind it. Key concepts of some popular ML techniques, including deep learning, along with hands-on exercises will be provided to students. By the end of this course, students will be able to apply a variety of ML algorithms to practical

    Instructor

    Applications Covered

    • Object Detection
    • Digital Recognition
    • Face Recognition
    • Self-Driving Cars
    • Medical Image Segmentation
    • Covid-19 Prediction
    • Spam Email Detection
    • Spectral Signal Categorization

    Tools Covered

    • Python
    • scikit learn
    • TensorFlow
    • Keras
    • Open CV
    • pandas
    • matplotlib
    • NumPy
    • seaborn
    • ANACONDA
    • jupyter
    • SPYDER

    Download the course description flyer:

    Download

    Volunteers Needed for Augmented Reality Study

    by Department of Computer Science

    We are looking for volunteers to take part in a study exploring how people may interact with future Augmented Reality (AR) interfaces. During the study, you will record videos of yourself tapping on a printed keyboard. The study takes approximately one hour, and you will be paid $15 for your time. You will complete the study at your home.

    To participate you must meet the following requirements:

    • You must have access to an Android mobile phone
    • You must have access to a printer
    • You must be a fluent speaker of English
    • You must be 18 years of age or older
    • You must live in the United States

    If you would like to take part, please contact rhabibi@mtu.edu


    Dr. Qun Li to Present Lecture April 23, 3 pm


    The Department of Computer Science will present a lecture by Dr. Qun Li on Friday, April 23, 2021, at 3:00 p.m. Dr. Li is a professor in the computer science department at William and Mary university. The title of his lecture is, “Byzantine Fault Tolerant Distributed Machine Learning.”

    Lecture Title

    Byzantine Fault Tolerant Distributed Machine Learning

    Lecture Abstract

    Training a deep learning network requires a large amount of data and a lot of computational resources. As a result, more and more deep neural network training implementations in industry have been distributed on many machines. They can also preserve the privacy of the data collected and stored locally, as in Federated Deep Learning.

    It is possible for an adversary to launch Byzantine attacks to a distributed or federated deep neural network training. That is, some participating machines may behave arbitrarily or maliciously to deflect the training process. In this talk, I will discuss our recent results on how to make distributed and federated neural network training resilient to Byzantine attacks. I will first show how to defend against Byzantine attacks in a distributed stochastic gradient descent (SGD) algorithm, which is the core of distributed neural network training. Then I will show how we can defend against Byzantine attacks in Federated Learning, which is quite different from distributed training.


    Article by Sidike Paheding in Elsevier’s Remote Sensing of Environment


    An article by Dr. Sidike Paheding, Applied Computing, has been accepted for publication in the Elsevier journal, Remote Sensing of Environment, a top journal with an impact factor of 9.085. The journal is ranked #1 in the field of remote sensing, according to Google Scholar.

    The paper, “Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning,” will be published in Volume 260, July 2021 of the journal. Read and download the article here.

    Highlights

    • A machine learning approach to estimation of root zone soil moisture is introduced.
    • Remotely sensed optical reflectance is fused with physical soil properties.
    • The machine learning models well capture in situ measured root zone soil moisture.
    • Model estimates improve when measured near-surface soil moisture is used as input.

    Paheding’s co-authors are:

    • Ebrahim Babaeian, Assistant Research Professor, Environmental Science, University of Arizona, Tucson
    • Vijay K. Devabhaktuni, Professor of Electrical Engineering, Department Chair, Purdue University Northwest, Hammond, IN
    • Nahian Siddique, Graduate Student, Purdue University Northwest
    • Markus Tuller, Professor, Environmental Science, University of Arizona

    Abstract

    Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm3 cm−3. Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm3 cm−3 and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination.


    Remote Sensing of Environment (RSE) serves the Earth observation community with the publication of results on the theory, science, applications, and technology of remote sensing studies. Thoroughly interdisciplinary, RSE publishes on terrestrial, oceanic and atmospheric sensing. The emphasis of the journal is on biophysical and quantitative approaches to remote sensing at local to global scales.


    AI, Mobile Security Grad-level Research Assistant Needed

    Dr. Xiaoyong (Brian) Yuan and Dr. Bo Chen are seeking an hourly paid graduate research assistant to work in the areas of artificial intelligence and mobile security. The project is expected to begin Summer 2021 (5/10/2021).

    Preferred Qualifications:
    1.     Passion for research in artificial intelligence and mobile security.
    1.     Familiar with Android OS and Android app development.
    2.     Basic knowledge of machine learning and deep learning.
    3.     Solid programming skills in Java, Python, or related programming languages. 
    4.     Experience with popular deep learning frameworks, such as Pytorch and Tensorflow is a plus.

    To Apply: Please send a resume and a transcript to Dr. Yuan (xyyuan@mtu.edu).


    GenCyber Teacher Camp Is July 19-23, 2021


    An NSA/NSF GenCyber Cybersecurity Teacher Camp for K-12 teachers will take place at Michigan Tech the week of July 19 – 23, 2021. This residential camp is offered at no cost to all participants.

    Topics include fundamental security knowledge, cyber hygiene, and other topics such as email phishing, password management, and cyber ethics. Participants will also learn how to develop lesson plans to teach cybersecurity in K-12.

    Room and board are included. Each teacher participant will receive a stipend of $500 for attending and completing camp activities. Commuting is also possible. Camp activities will count for 25 State Continuing Education Clock Hours (SCECH).

    Find complete details and apply here.  The application deadline is May 1, 2021.

    Funding of the camp is provided jointly by the National Security Agency (NSA) and the National Science Foundation (NSF) through a grant award led by Professor Yu Cai and Tim Van Wagner, both from the College of Computing Department of Applied Computing.

    Watch a video from the 2019 GenCyber Teacher Camp below.

    Gencyber Teacher Camp @ Michigan Tech 2019


    Assistants, Helpers Needed for Cybersecurity Teacher Camp, July 19-23


    Dr. Yu Cai, Applied Computing, is seeking motivated students to help with this summer’s GenCyber Teacher Camp, which takes place on campus July 19-23, 2021.

    1. Twenty K-12 teachers attending the camp.
    2. Students will work as teaching assistants and camp helpers. They will set up the lab, help during hands-on activities and games, manage the website, and help the assessment. Students will be paid for 3 weeks of work during July.
    3. Contact Dr. Yu Cai (cai@mtu.edu) for details and to apply.

    Michigan Tech Ranked Among the Best

    Two recent rankings place Michigan Tech among elite colleges and universities on both the state and national level. 

    Michigan Tech was rated #2 on the list of the Best Accredited Online Colleges in Michigan by EDsmart. The ranking service assesses online colleges in Michigan based on data that covers cost, academic quality, student satisfaction and salary after attending. 

    Michigan Tech was ranked #13 on the list of the 50 Best Value Public Colleges in America by Stacker. The ranking included only public, four-year colleges and weighed the cost of tuition with each school’s acceptance rate, quality of professors, diversity and the median earnings for alumni six years after graduation.


    MS Defense: Vijay Pathak

    by Mechanical Engineering

    Vijay Pathak (advisor Ghosh Susantra) a master’s in Mechanical Engineering student will present his master’s defense at 2 p.m. tomorrow (April 14) via Zoom.

    The title of the presentation is “Studying the Effects of Initial Crack Angle on the Crack Propagation in Graphene Nano-Ribbon Through Molecular Dynamics Simulations.”