MACHINE LEARNING CONFERENCE
Saturday, April 7, 2018
Hosted by Eastern Michigan University
Time and location:
8:30am - 3:30pm in room 216, Pray Harrold building
Conference Theme
The conference theme for this year is Deep Learning, Theoretical
and Practical Aspects.
Conference Goal: To
bring together faculty and students, to offer the opportunity of showing
their work to others, and to invite them to discussions and prospective
future cooperation.
Conference Fee
There is no conference fee this year. The Department of
Mathematics&Statistics covers all the costs involving the room and breakfast.
Conference Chair:
Ovidiu Calin, Department of Mathematics&Statistics, Eastern Michigan
University. If you have any questions please contact the conference chair.
Conference Program:
8:30 - 9:00
Registration and Breakfast, location: entrance hall of Pray Harrold
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9:00 - 9:15 |
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10:30 - 11:00 Aaron Bolton and Praveen T W Hettige, students, EMU: Classifying Mosquito Sounds Using Machine Learning Techniques Abstract: Anopheles mosquitoes are the primary vector of malaria. We use Support Vector Machines, K-Nearest Neighbors and Random Forest to classify 5 mosquito species by features extracted from their wing-beat sounds. Our accuracy is roughly 91% for SVM, 87% for KNN and 89% for Random Forest. |
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11:40 - 12:15 Henry Han, faculty, Fordham University: Implied Volatility Pricing via Integrative Learning Abstract: With the surge of massive data in finance, implied volatility pricing remains a challenge for its essential role in trading, though few model-driven methods are available in the literature. In this work, we proposed a data driven implied volatility analytics by inventing a novel integrative learning approach. The proposed method integrated different machine learning models to price impolied volatility for various in-the-money options by leveraging the availability of a large amount of data in the market. The proposed approach not only demonstrates its superiority in prediction accuracy, but also a strong model independence by overcoming the generalization issue of traditional model-driven approaches. |
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12:15 - 13:15 Lunch break |
13:15 - 13:55 Muhammad Sohaib Arif, student, EMU: YOLO for impact detection in Ice Hockey. Abstract: YOLO is short for You Only Look Once. This is method that divides an image into a grid and retrieves bounding boxes for each grid using convolution and then regression. The box is comprised of 5 items that are coordinates of the center in x and y, relative height and width of the box and category of the object detected. I used this method with a dataset that I developed from 3 videos and timestamps that were taken from sensors and then corroborated manually by someone else at EMU. Slides here |
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13:55 - 14:20 Syed Hussain, student, EMU: Abstract: I will present a talk on YOLO (You Only Look Once) object detection model on the LISA Traffic Sign Dataset with an enhancement of the original Data set using a neural network on the Data set which will be for certain weather conditions or for night time. This modification will double the size of the Data set and will be able to detect the Traffic Signs for different weather conditions such as Snow or Rain. This is a joint project with Sohaib Arif also from the Computer Science Department. |
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14:50 - 15:30 Andrew Ross, faculty, EMU: Ethics, Public Policy, and Machine Learning Abstract: I will discuss many issues in the ethical use of machine learning, and how it interacts with public policy. Examples include automated parole recommendations and car insurance pricing. We will also talk about general characteristics of contexts that can use ethical/public policy issues in machine learning, and common traps like data that is biases based on existing societal influences. This talk is inspired and based on the book "Weapons of Math Destruction" by Cathy O'Neill and other books. |
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See you again next year!