Computer Science 682 - Neural Networks: Modern Intro

This course will focus on modern, practical methods for deep learning. The course will begin with a description of simple classifiers such as perceptrons and logistic regression classifiers, and move on to standard neural networks, convolutional neural networks, and some elements of recurrent neural networks, such as long short-term memory networks (LSTMs). The emphasis will be on understanding the basics and on practical application more than on theory. Most applications will be in computer vision, but we will make an effort to cover some natural language processing (NLP) applications as well, contingent upon TA support. The current plan is to use Python and associated packages such as Numpy and TensorFlow. Prerequisites include Linear Algebra, Probability and Statistics, and Multivariate Calculus. All assignments will be in the Python programming language.

Open to graduate students in Computer Science, Electrical & Chemical Engineering, Linguistics, Math, and Statistics. PREREQUISITES: BASIC FAMILIARITY WITH AI/COGNITIVE SCIENCE/MACHINE LEARNING. BASIC KNOWLEDGE OF GRADIENT-BASED OPTIMIZATION. CALCULUS, LINEAR ALGEBRA, ALGORITHMIC ANALYSIS, PROBABILITY/STATISTICS, PROGRAMMING IN PYTHON OR EQUIVALENT HIGH LEVEL LANGUAGE. SEATS HELD FOR INCOMING STUDENT REGISTRATION. STUDENTS NEEDING SPECIAL PERMISSION MUST REQUEST OVERRIDES VIA THE ON-LINE FORM: https://www.cics.umass.edu/overrides.