CSCI 4352 Machine Learning
Fall 2019
Meeting time and location
EIEAB 1.206, Tue and Thu 12:30 pm ~ 1:45 pm
Office hours
EIEAB 3.244, Tue and Thu 10:45 am ~ 11:45 am
Course Description
This course provides selected topics in Machine Learning focusing on Deep Learning. Topics include: logistic regression, neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, generative adversarial networks, regularization, decision tree, boosting, feature extraction, Bayesian network classifiers, and clustering techniques. The course also explores the applications of such topics in Data Science, Data Mining, and Data Analytics. Through projects, the taught algorithms are used to build intelligent systems in areas such as smart robots, computer vision, medical and healthcare informatics.
Prerequisites
CSCI 3333 or consent of instructor.
Learning Objectives
The course covers the principles, design and implementation of learning programs that improve their performance on some set of tasks with experience. Upon successful completion of the course, students will have a broad understanding of machine learning algorithms and their use in data-driven knowledge discovery and program synthesis. Students will have designed and implemented several machine learning algorithms in Python and Google Tensorflow deep learning package. Students will also be able to identify, formulate and solve machine learning problems that arise in practical applications.
Grading Policies
Programming Assignments 50% Project 50%
Course grades will be decided according to the following scheme:
A (100~90), B (90~80), C (80~70), D (70~60), F (60~0)
Course Materials
All course materials are on UTRGV Blackborad (https://my.utrgv.edu/web/myutrgv/home)
Team Project
Team 1: Johnny Marroquin, Edgar Gutierrez - Proposal[
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Team 2: Sergio Hinojosa, Robin Belo - Proposal[
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Team 3: Marcos Pena, Leonel Pena - Proposal[
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Team 4: Juan Martinez, Aaron Cuadras - Proposal[
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Team 5: Domingo Martinez Jr., Gabriel Aguinaga - Proposal[
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Team 6: Tarana Mou, Gabriela Garza - Proposal[
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Team 7: Victor Martinez, Hector Lopez - Proposal[
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Team 8: Alyssa Gillen, Isaiah Saucedo - Proposal[
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Team 9: Andrea Cantu, Alyssa Cantu - Proposal[
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Team 10: Joselito Guardado, Eleazar Del Toro - Proposal[
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Team 11: Lauryn Brough, Francisco Gonzalez - Proposal[
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Team 12: Mauricio Flores, Kevin Kowalski - Proposal[
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Team 13: Lorenzo Aleman, Brandon Gibbs - Proposal[
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Team 14: Damon Mohan, Paulo Uchoa - Proposal[
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Team 15: Ignacio Alanis, Julio Aguilar - Proposal[
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Team 16: Adrian Del Bosque, Kevin Jackson - Proposal[
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Team 17: Ricardo Carrillo, Jamila Bunagan - Proposal[
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Team 18: Antonio Chalons, Joshua Garza - Proposal[
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Team 19: Liam San Pedro, Joel Rodriguez - Proposal[
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Team 20: Jose Isaac Cuevas, Rafael Abraham Cuevas - Proposal[
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Team 21: Miguel Mascorro, Victor Ortega - Proposal[
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Team 22: Joshua Harding, Lawrence Hernandez - Proposal[
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Team 23: Mario Velasquez - Proposal[
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Spring 2019
Team Project
Team 1: Austin Luchsinger, Salvador Jimenez - Proposal[
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Team 2: Mauricio Ortiz, Francisco Saldivar - Proposal[
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Team 3: Ryan Luna, Rene Reyes - Proposal[
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Team 4: Angel Cantu, Nami Akazawa - Proposal[
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Team 5: Luis Romo, Marissa Jain - Proposal[
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Team 6: Andrew Chen, Paulina Varela - Proposal[
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Team 7: Homero Benavides Ramirez, Juan Cantu - Proposal[
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Team 8: Edgar De La Torre Bazan - Proposal[
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Team 9: Jacob Handy, Austin Karingada - Proposal[
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Team 10: Anthony Meza - Proposal[
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Team 11: Adrian Gonzalez, David Parra - Proposal[
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Team 12: Maria Garza, Fernando Martinez - Proposal[
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Team 13: Osvaldo Castellanos - Proposal[
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Team 14: Atieh Poushneh - Proposal[
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Team 15: Louis P. Dartez
Team 16: Adolfo Gonzalez - Presentation[
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