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  • Category: Seminars

    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

    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.”

    Join the virtual lecture here.

    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.


    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. The 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.

    GenCyber Cybersecurity Teacher Camp Is July 19-23

    by Yu Cai, College of Computing

    A GenCyber Cybersecurity Teacher Camp for K-12 teachers will be held at Michigan Tech during the week of July 19 – 23. Participants will learn cyber hygiene and fundamental security knowledge including email phishing, password management, and cyber ethics. Participants will also learn how to develop lesson plans to teach cybersecurity in K-12.

    This is a residential camp (commuting optional), and is offered at NO COST to all participants. Room and board is included. Each teacher participant will receive a stipend of $500 for attending and completing camp activities. Camp activities will count for 25 State Continuing Education Clock Hours (SCECH).

    Click here for more information and to apply. The application deadline is May.

    Funding for the camp is provided jointly by the National Security Agency (NSA) and the National Science Foundation (NSF) through an award led by Yu Cai and Tim Van Wagner from the College of Computing.