Machine Learning

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IEEE Florida West Coast Section - Consultants Network Affinity Group (CNAG)

Attendees will receive 3 CEHs.

Certificate available by selecting YES in response to the CEH Certificate question.


Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from data without being explicitly programmed. It's about equipping systems with the ability to learn, identify patterns, and make decisions based on data, rather than solely relying on pre-defined instructions.

 



  Date and Time

  Location

  Hosts

  Registration



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  • St. Petersburg Yacht Club
  • 11 Central Ave, St. Petersburg, FL 33701
  • St. Petersburg, Florida
  • United States 33701

  • Contact Event Hosts
  • Starts 10 June 2025 04:00 PM UTC
  • Ends 22 August 2025 04:00 AM UTC
  • Admission fee ?


  Speakers

Stephen

Topic:

Machine Learning

Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from data without being explicitly programmed. It's about equipping systems with the ability to learn, identify patterns, and make decisions based on data, rather than solely relying on pre-defined instructions.

Biography:

Stephen Skrzypkowiak, SMI EEE, PhD, PE Consultant:  Technical Lead and SME supporting the Science and Technology (S&T) Directorate of the Department of Homeland Security (DHS), the Transportation Security Administration (TSA), the Transportation Security Laboratory (TSL) and the Federal Aviation Administration (FAA) over the past 15 years as a Subject Matter Expert (SME) in x-ray physics, image processing, computer vision, and detection algorithm development over the full product life cycle of security screening systems.

  Prior to 2003, served as System Architect in private industry/commercial environment for the development of x-ray-based explosives detection systems, including the development of detection, segmentation and reconstruction algorithms. Project Lead in collaborative efforts with geographically dispersed teams of software, electrical and mechanical engineers and university researchers. Adept at creating and driving solution for complex technical challenges with limited deadlines and budgets. Extensive work in government and commercial contracting, work plan development, and project management.

Directed research and development in reconstruction methods, computer vision, pattern recognition, signal processing, imaging processing, medical image analysis, video compression, and x-ray scanning systems. Software coded with VC++, Visual Studio and MATLAB. Extensive experience in file format standard establishment and definition in the DICOM and DICOS standards under the oversight of NEMA and the EDS N42.45 Image Standard under the oversight of the IEEE.

Project Engineer in the design and testing of the L3 Technology eXaminer 3DX 6000 system, the eXaminer 3DX 3000 and the eXaminer 3DX 1000 ARGUS EDS, including hardware and software architecture, the implementation of the real-time detection algorithm, the baggage viewing station and the image archiving system for the second generation certified systems.

 





Agenda

1.      Machine Learning and relationship to the Artificial Intelligence circle

2.      Defining Machine Learning

3.      Need for Machine Learning

4.      Difference between Machine Learning, Traditional Programming, and Artificial Intelligence.

5.      Workings of Machine Learning algorithms.

6.      The Machine Learning Lifecycle

7.      Types of Machine Learning

8.      Various Applications of Machine Learning

9.      Limitations of Machine Learning

10.  Machine Learning Future

11.  Deep Learning and how it relates to Machine Learning

12.  Difference between Machine Learning and Deep Learning

13.  Deep Learning Workings

14.  Deep Learning in Machine Learning Paradigms

15.  Evolution of Neural Architecture

16.  Deep Learning Applications

17.  Challenges in Deep Learning

18.  Advantages/Disadvantages of Deep Learning

19.  Software/models for development

20.  Deep Learning Future

 



The menu for these CNAG presentations will be Pizza (Meat Lovers, Veggies, cheese)  and soft drinks (Coke, Diet Coke, etc.)  and it is included in the price along with parking at the St. Petersburg Yacht Club.