3/3/21: Unsupervised and Causal Learning Intro
Professor Chris Rogan gave a seminar about unsupervised and causal learning. His lecture can be found below. Your browser does not support the video tag.
Meetings
2021
2/5/21: Reinforcement Learning with Chris Harvey and ML at Google and Uber with Brian McClendon
Chris Harvey gave a seminar about reinforcement learning. His lecture can be found below and slides can be found here. Your browser does not support the vid...
2/4/21: Quantum Machine Learning with Professor Elizabeth Behrman
Professor Elizabeth Behrman (Wichita State) came to talk about her research in Quantum Machine Learning. Her lecture can be found below and her slides can be...
2/3/21: Why Programming is Important with Chris Harvey
The recording of Chris’s talk is below. His Jupyter notebook can be downloaded here.
2/2/21: Competition Details
Hey everyone!
2/2/21: Intro To Neural Networks and Tensorflow with Chris Harvey
Chris’ talk highlighted the basic mathematical structure of neural networks, as well as how to implement them with Tensorflow using Keras. His Jupyter notebo...
2/1/21 Spring Kickoff Day 1: Intro to Probability and Installing ML Libraries
Intro to Probability and Neural Networks with Professor Rogan Your browser does not support the video tag. If you cannot access the video, you can downloa...
2020
10/13/20: Not Just Machine Learning: Ethical Considerations and Machine Learning with Professor Cameron Piercy
This week, Professor Cameron Piercy joins us from KU Communication studies. Dr. Cameron Piercy from KU Communication Studies and the Human-Machine Communicat...
09/08/2020: Introduction to Software Management with Dalton Hahn
We had EECS PhD student Dalton Hahn give an introduction to software management and version control with Git and GitHub. His slides can be found here and a r...
07/31/20: Classifying Radio Galaxies with Machine Learning by Xinyu Mai
This week, Xinyu Mai discussed her undergraduate research in classifying radio galaxies. Slides from this lecture can be found here. A video recording of the...
07/17/20: Machine Learning in High Energy Physics by Justin Anguiano
This week, Justin Anguiano gave a talk on machine learning in high energy physics. Slides from this lecture can be found here. A video recording of the prese...
1/30/20: Introduction to Statistics Lecture by Professor Rogan
This week we had an introductory statistics lecture by Professor Rogan. Notes from this lecture can be found here.
2019
12/3/19: Machine Learning to Augment Material’s Property Prediction with Professor Hartwin Peelaers
This week, we learned about how machine learning can help condensed matter physicists predict properties of certain materials. Professor Peelaers talked abou...
11/12/19: Deep Learning at the LHC with Professor Javier Duarte
Professor Javier Duarte visited us this week from University of California, San Diego. He discussed his research with machine learning in high energy physics...
11/6/19: Can A Machine Tell Me Who Is A Better Impersonator? with Dr. Midam Kim
This week, we learned about Dr. Midam Kim’s research in linguistics and its applications in business. Her research in linguistic impersonation is one of the ...
10/23/19: Mining Imperfect Data with Professor Jerzy Gryzmala-Busse
This week we enjoyed a lecture on mining imperfect data by Professor Gryzmala-Busse from the Department of Electrical Engineering and Computer Science. Slide...
10/16/19: Brainstorming Part 2
This week, we continued brainstorming about our collaborative project. See notes from this session here.
10/2/19: Species Identification Using Tensorflow by Ali Khalighifar
In this presentation, Ali summarized his research – using deep neural networks (TensorFlow) to design species identification systems – in two sections: (1) m...
09/25/19: Neural Network Tutorial
Summary and notes from this session coming soon! You can check out the intro tutorial here and a follow up tutorial on gradient descent here.
09/18/2019: Introduction to Machine Learning Lecture with Prof. Rogan
To start of this meeting, we officially (and unanimously!) ratified the LML Constitution, which can be found here. For quorum purposes, we had 16 active memb...
9/11/19: Linear Regression Tutorial by Dr. Sadia Khalil
Regression We learned two models for regression. A simple linear regression as an example of a parametric method, and Gaussian Process as an example for the ...
8/7/19 Paper Discussion: Generative Adversarial Networks
This week, John and Quinn lead a discussion on Generative Adversarial Networks (GANs), networks consisting of two opposing subnetworks that train against eac...
7/31/19 Paper Discussion: Batch Normalization
This week Micah led us in a discussion this paper on batch normalization (BN), which is a technique used in machine learning to speed up learning accurately....
7/24/19 Tutorial: Tensorflow
We took a break from our regular paper discussions this week to host a tutorial on Tensorflow, a popular machine learning open-source software library. This ...
7/17/19 Paper Discussion: Quantum Machine Learning
We discussed the paper Quantum Machine Learning by Jacob Biamonte, et. al. Ryan and Ty led our discussion, which mainly focused on the feasibility of quantum...
7/10/19 Paper Discussion: Conditional Neural Processes
This week, Dr. Sadia Khalil and Mitzi Sanchez Cubedo led a discussion surrounding the paper, Conditional Neural Processes by Marta Garnelo, et. al.
7/3/19: Intro to ML and Linear Regression Tutorial
This week, we split our time between learning about the mathematical basis of machine learning with Professor Chris Rogan and applying this basis in a Python...
First Summer 2019 Meeting
In our first meeting of the summer, we enjoyed a lecture from Professor Chris Rogan about statistics in the context of machine learning.