To get an idea of presentations we have hosted in the past, please see the list below. To sign up and see future events, please visit our meetup page.
May 30th, 2018 – AI & Machine Learning: From Hype to Real World Applications
Time for another segment of the Infinite Possibilities Series! We’ll begin with a high level overview of Machine Learning from our local interest group. Next an impressive array of presenters will discuss the algorithm that makes it all possible, explore job automation and elimination, and show off how a local startup is incorporating machine learning. You’ll also have the chance to hear how AI and predictive analytics are being incorporated in the surgical field. As always, we’ll dive into the ethical implications of the technology — is AI about to take over YOUR job?
Phillipe Loher, Founder Machine Learning Group – Intro to Machine Learning
Aru Anavekar, CEO and co-founder of Botsplash, a SaaS conversational messaging platform focused on improving communication between consumers and businesses.
Allyson Cochran, Keith Murphy, and Dr. William Lyman from Carolinas Center for Surgical Outcomes Science (CCSOS), a recently-created division within Atrium Health’s Department of Surgery. They will discuss how analytics, such as artificial intelligence (ML/DL) and predictive analytics, are being incorporated into surgery.
Dr. Wlodek Zadrozny from UNCC’s computer science department, will showcase what leading researchers in Natural Language Processing are working on. He will discuss the work at UNCC’s Data Science Initiative Program, including some of his students’ research in machine learning, and share his experiences developing IBM Watson to win at Jeopardy.
April 10, 2018 – NC Science Festival Event – AI & Machine Learning
Come see live demonstrations of our groups AI/Machine Learning projects. We will also show how Machine Learning is helping with cancer. We will also have a couple hands on Machine Learning stations. No prior knowledge of algorithms or machine learning required!
Some examples of what will be demoed:
– see how our machines take any photo and paint it just like Picasso, Rembrandt, and Monet would have! Can machines be even better artists?
– aiding in classifying cancer origins
– our Machine Learning car that can guess what it see’s
– algorithms that learn on their own without even knowing the rules
– guessing what a picture is
This weeks event is proudly part of the NC Science Festival (http://www.ncsciencefestival.org/) helping inspire future generations in the sciences.
March 13, 2018 – It’s Pi Day with Monte Carlo simulations!
Come celebrate hopefully the geekiest Pi day event in the area! Pi (3.14159) day coincidentally falls the day after this event – Learn about the history of both Pi and Monte Carlo simulations. Resume where Pythagoras, Euclid & Archimedes of Syracuse left off and learn how to calculate Pi using a Monte Carlo simulation technique. New to 2018’s Pi Day – we will also show case how to calculate Pi using a Neural Net implementation of the Monte Carlo using Tensorflow!
Feb 13, 2018 – Create an algorithm that learns to play Pong by itself! – OpenAI & DeepRL
Learn how to create an algorithm that learns to play Pong on its own. It will learn to play the game without any instructions at all! Come learn about OpenAI, deep reinforcement learning (DeepRL), and policy gradients. This presentation will give a step by step walkthrough on how a reinforcement algorithm works.
p.s. Iron man Elon Musk (the Tesla & SpaceX guy) helped start OpenAI.org
Slides here: https://machinelearning.group/index.php/slides-from-pong-playing-openai-reinforcement-learning-algorithm-presentation/
Jan 11th, 2018 – Panel Chat – Data, AI, Machine Learning, IoT : Bringing it all together
Charlotte Bots and AI is co-hosting the January meetup along with Davidson Machine Learning Group are arranging a Panel Chat with some of the very indulged and involved personalities in Charlotte. Most of these folks have diverse interest, passion and manage to dedicate time to what they enjoy.
At the Panel Chat we will be exploring the different areas and spectrum of Data Driven Technology such as – Machine Learning, Artificial Intelligence, Automation, Data Mining, Internet Of Things(IoT).
Why is Data essential? How is Data interpreted and analyzed, Penalty of incorrect analysis and predictions? Patterns and anomaly detection techniques? IoT – What and How? and more…
Speaker info here: https://www.meetup.com/Davidson-Machine-Learning-Meetup/events/245792431/
Nov 7th, 2017 – Machine Learning using Google technology (co-presentation with Google)
Presented by: Brian Vinson (Google) & Phillipe Loher
Come learn about Machine Learning on the Google Cloud, Kaggle contests, and Tensorflow.
(a) Google will present on how you can incorporate prebuilt Machine Learning APIs, such as those for vision and speech, into your projects.
(b) As part of a world-wide Google Kaggle vision contest, our group (your local and free Machine Learning Group) created the ‘hottest’ (most viewed/used) starterkit on how to solve vision classification problems using Google’s Tensorflow library. Come learn about the details and a sneak peak into the source code that allowed a computer how to tell the difference between a cat and a dog. This technology is helping with self-driving cars and the fight against cancer.
Oct 17th, 2017 – Science and Technology – Learn to build your own AI/Machine Learning car
Presented by: Phillipe Loher
Description: Learn how to build your own remote controlled Machine Learning car using a Rasberry Pi 3. This car can talk and is able to recognize objects! Come learn about each step that was used to build this car physically as well as hooking it up on the software side. An in-silico neural brain was added to the car so that it can recognize objects visually. Details of how this was done will be discussed in detail. There is also an easter egg feature that will be demoed!
Sept 27th, 2017 – AI Vision, NeuralStyle & UNCC Data Science Social @ UPTOWN Location
Presented by: Phillipe Loher
Description: Come learn about the state of the art advances in Computer Vision using Machine Learning/AI techniques. These algorithms are allowing computers to outperform humans in computer vision and are being used as tools for self-driving cars and cancer research. Some fundamental low level details of Convolutional Networks and the NeuralStyle algorithm will be discussed. The NeuralStyle algorithm allows a computer to ‘paint’ like Picasso. Some UNCC students (part of the University’s Data Science Initiative program) will be there so we hope it’s great networking event for all inspiring data scientists.
July 11th, 2017 – Intro to Apache Spark and Machine Learning use cases in the finance industry
Presented by: Dinesh Arora
Description: Come see an introduction of Apache Spark and a high level overview of Machine Learning use cases in the finance industry.
June 27th, 2017 – DataRobot for Machine Learning
Presented by: Vijay Rajan
Description: DataRobot will present their machine learning platform. DataRobot is a machine learning platform that combines the best tools from open source technologies like scikit learn, R, Google tensorflow, spark, H2O and others. By automating the technical work like model tuning, cross validation, feature selection, and model deployment, DataRobot brings data science within the reach of a much larger audience and provides a way to make trained data scientists much more productive. Join us for a demonstration of the platform, followed by Q&A.
May 30th, 2017 – Neural Bots with Azure and Google’s Tensorflow. UPTOWN location @ Google Fiber
Presented by: Pon ArunKumar Ramalingam & Phillipe Loher
Description: Come see how adding custom Neural Networks to your bot will allow it to understand the contents of images and even allow it to paint like Monet and Picasso. We will give an overview of creating a chat bot using the Azure Bot Framework, give business use cases, and showcase advanced Neural Network examples that utilizes Google’s Tensorflow machine learning library.
April 11th, 2017 – NC Science Festival Event: Can computers paint like Picasso and Rembrandt?
Presented by: Phillipe Loher
Description: Come see our machines take any photo and paint it just like Picasso, Rembrandt, and Monet would have! Can machines be even better artists? We will give a glimpse into the world of neural networks and how this technology is applicable to help with areas such as cancer. No prior knowledge of algorithms or machine learning required!
Mar 14th, 2017 – It’s Pi Day with Monte Carlo simulations!
Presented by: Phillipe Loher
Come celebrate hopefully the geekiest Pi day event in the area! Pi (3.14159) day coincidentally falls on our Tuesday 3.14 – Learn about the history of both Pi and Monte Carlo simulations. Resume where Pythagoras, Euclid & Archimedes of Syracuse left off and learn how to calculate Pi using a Monte Carlo simulation technique. Additionally, a non-Pi related Monte Carlo will be showcased.
Feb 18th, 2017 – Presentation on Titanic and Cancer Classification with Random Forests
Presented by: Rich Brosius
Learn how the popular Random Forest ensembl algorithm works. See how it can be used to predict survivors on the RMS Titanic and a glimpse on how it’s helpful in classifying cancers. An example implementation using Python + sklearn will be made available during the talk.
January 17th, 2017 – Presentation on Genetic Algorithms
Presented by: Scotty Chung
No prior knowledge of algorithms, genetics, or particle accelerators is required. Scotty will be presenting an overview of Genetic Algorithms. He hopes to answer introductory questions such as What are they, How do they work, When are they used. He will also explain the implementation for minimizing beam loss he performed at the Spallation Neutron Source at ORNL and share his experience from that project.
Scotty Chung is a biomedical engineer by education and data enthusiast by interest. Most recently a software consultant for a research database application, he is wishing to pursue a graduate program at Wake Forest investigating injury biomechanics. Having found the Davidson Machine Learning group, he’s enjoyed learning from experienced data scientist and hobbyist alike.
November 15th, 2016 – Amazon’s Echo Dot, Programming Alexa – Home Automation – Machine Learning
Phillipe Loher will present on his experience with Internet of Things (IoT) device, Amazon Echo Dot. A demo will be shown on how a private ‘custom skill’ was added to Alexa to communicate the status of a Solar Panel system. Amazon Lambda will also be briefly discussed which hosts the custom skill.
October 25th, 2016 – NLP Series Part 3 (IBM Watson & Google Syntaxnet)
(1) Intro to NLP using IBM Watson. Resources to help the audience self-start will be included – Presented by Sam Sharma
(2) Overview and hands-on demonstration of using Google SyntaxNet (Worlds Most Accurate Parser) – Presented by Phillipe Loher
October 18th, 2016 – NLP Series Part 2 (Word Representations)
Presented by Phillipe Loher. Technical overview of one-hot vectors & word embeddings (via Word2Vec)
October 11th, 2016 – NLP Series Part 1: Chat Bots
Jerry Hamby (short bio below) will give a demo/presentation on his award winning chat bot prototypes. This will be our entry into the Natural Language Processing (NLP) series of meetups.
Jerry Hamby: Mobile app developer, iOS, Android and Adobe Flex. Recently started developing chat bot API’s with Node.js. For many years was located in Santa Monica, California, while in LA worked with many movie studio including Disney, developing multimedia engines for marketing and promotions. Also while in LA ,was a adjunct professor at Santa Monica College teaching app development. Saw this somewhere on the internet: “we have gone through the web generation, the app generation and we are now entering the chat bot/AI generation”. I fully subscribe to this predication.
September 27th, 2016 – Industry Series (Crime Scenes)
This meetup is not at the usual location/time. It will be in Huntersville starting at 8pm. Please see meetup page for details.
This meetup is part of our Industry Series and will help us discuss how to apply Machine Learning to crime scene and other police/detective work.
Dale Callan (full bio here) has generously volunteered to give the Davidson Machine Learning group a tour! Dale is a retired Federal agent who is currently program Manager of General Forensics at the Forensic Academy at CPCC’s Merancas Campus in Huntersville, NC. The academy trains police officers, Detectives and CSI’s from 11 different states.
A tour of our forensic academy will include the following:
(a) Crime scene rooms- apt-motel room, office, pawn sho
(b) Court room
(c) General forensic lab
(d) Firearms simulator
(e) Digital lab- large investment of cell phone and computer forensic hardware
September 13th & 20th, 2016 – Kaggle & Python Series
a. By: Rich Brosius. Bring your laptops if you can! Rich will be going over the details of enrolling & participating in Kaggle competitions. This includes understanding the submission process, leadership board, and how/when to use Kaggle Kernels. Some popular Python packages will also be discussed in the samples. He will also be going over a Random Forest example on the Titantic Kaggle dataset.
b. By: Max McCann, Phillipe Loher, Rich Brosius. Presentation on this past weekends Machine Learning Hackathon entry from the Davidson Machine Learning Group
September 6th, 2016 – RDF Triplestore & Govt Budget Visualizations
(1) RDF Overview
Presented by: John Chachere
A triplestore or RDF store is a purpose-built database for the storage and retrieval of triples through semantic queries . Get a good overview and discuss its applications in Machine Learning!
(2) Visualization of Government Budgets
Presented by: Mike Seman
Mike is working on projects using R, Shiny, and the d3.js library. Get a quick overview of his work using these tools to help analyze government budgets!
August 23rd, 2016 – Intro to Monte Carlo simulations!
Presented by: Phillipe Loher
Learn about the history of both Pi and Monte Carlo simulations. Resume where Pythagoras, Euclid & Archimedes of Syracuse left off and learn how to calculate Pi using a Monte Carlo simulation technique.
See presentation at: https://github.com/phillipeloher/LowellMakesBigData/blob/master/MapReduceCourseSlides_forGitHub.pdf . The BigData/MapReduce portion will be saved for another time.
August 16th, 2016 – Get running with TensorFlow, Cloud9, and Kaggle
Presented by: Max McCann, Phillipe Loher, Rich Brosius
a. Learn what TensorFlow is and how to program in it
b. Deploy a working TensorFlow example (Not-MINST) using Cloud9 (https://c9.io/) for quick and easy development
c. Kaggle competition intro and contest selection for those that want to try a group competition!
August 9th, 2016 – Gradient Descent Explained (continued)
Presented by: Rich Brosius
To view the math behind gradient descent ahead of time, please visit: https://groups.google.com/forum/?utm_medium=email&utm_source=footer#!msg/davidson-machine-learning/5NXWqrGegho/0fYAYxk4AQAJ
August 2nd, 2016– Back Propagation & Gradient Descent – Go In Depth
Presented by: Physics expert and group member, Rich Brosius!
Details of demonstration:
Artificial neural networks inspired by the human brain have enabled some of the most remarkable advancements in machine learning within the last decade. Today neural networks can be taught to perform in image and speech recognition, finance models, search engines, self driving cars and a variety of other tasks.
Training a neural network requires an optimization process called backpropogation. In this presentation we will examine gradient descent, the most efficient means of training a neural network through supervised learning. We will cover the theory of operation and dive into the mathematics with a numerical example demonstrating a single forward and backward pass through the network. We will then demonstrate how this iterative process can be repeated thousands of times to achieve the desired behavior.
July 25th, 2016 – ConvNet’s, NeuralStyle algorithm, & Pokemon
Presented by: Phillipe Loher & Max McCann
Learning and capitalize on the latest Pokemon craze, we will be doing informal demonstrations Neural Networks, ConvNets, NeuralStyle algorithm, and Pokemon Morph. See blog entry here for more details.