COMPSCI 682 Neural Networks: A Modern Introduction

This 3-credit 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. Prerequisites include Linear Algebra, Probability and Statistics, and Multivariate Calculus. Assignments will be in Python and potentially some in C++.

Accommodation Statement

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.

Academic Honesty Statement

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent (


Prof. Erik Learned-Miller
Office Hours: Thu 3-4p, CS248

Teaching Assistant

Hang Su
TA Hours: Tue 1-2p & Wed 2-3p, CS207
Pia Bideau
TA Hours: Mon 10-11a & Fri 9-10a, CS207


Fall 2017
Tue & Thu 8:30-9:45 am
Hasbrouck 134

Grading Policy

Assignment #1: 15%
Assignment #2: 15%
Assignment #3: 15%
Midterm: 15%
Final Project: 40%

Course Discussions

Participate discussions on Piazza, and use extension to annotate online course notes inline.

Discussion Forum »


There will be 3 homework assignments through the semester.

View details »


Many thanks to Fei-Fei Li and Andrej Karpathy for graciously letting us use materials from the Stanford CS231n: Convolutional Neural Networks for Visual Recognition.