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Fall 2019 Electrical Eng/ Computer Science/ Biomedical Engineering E4040 section 001
NEURAL NETWRKS & DEEP LEARNING

Call Number 98712
Day & Time
Location
F 10:10am-12:40pm
207 Mathematics Building
Points 3
Grading Mode Standard
Approvals Required Department
Instructor Zoran Kostic - homepage
Type LECTURE
Method of Instruction Classroom
Course Description Prerequisites: (BMEB W4020) or (BMEE E4030) or (ECBM E4090) or (EECS E4750) or (COMS W4771) or equivalent. Developing features & internal representations of the world, artificial neural networks, classifying handwritten digits with logistics regression, feedforward deep networks, back propagation in multilayer perceptrons, regularization of deep or distributed models, optimization for training deep models, convolutional neural networks, recurrent and recursive neural networks, deep learning in speech and object recognition.
Web Site Vergil
Department Electrical Engineering
Enrollment 131 students (152 max) as of 12:16AM Tuesday, September 17, 2019
Subject Electrical Eng/ Computer Science/ Biomedical Engineering
Number E4040
Section 001
Division School of Engineering and Applied Science: Graduate
Open To Engineering and Applied Science: Undergraduate, Engineering and Applied Science: Graduate
Campus Morningside
Section key 20193ECBM4040E001

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SIS update 09/17/19 00:16    web update 09/17/19 08:19