Syllabus for MME 596
Seminar on Deep Learning

Summer 2019

Course description

A reading seminar on deep learning with the central text Introduction to Deep Learning by Eugene Charniak. Chapter exercise solutions and some supplemental reading will be required. Programming in Python with Tensorflow will be required, but experience with any programming language is sufficient.

General information

Rico AR Picone, PhD
Actual office hours (CH 103C)
T,Th 2:20–4:50
Virtual office hours (zoom, make appointment!)
T,Th 2:20–4:50
Virtual office hours appointments
make appointment
Office location
CH 103C



Eugene Charniak. Introduction to Deep Learning. MIT Press, 2018. (Required.)


The following schedule is tentative. All assignments will be set one week before the due date.

week topics introduced reading due
perceptrons, neural nets, feed-forward neural nets, Python EC Ch 1 get started on Assignment 1
feed-forward neural nets continue Assignment 1
Tensorflow EC Ch 2 Assignment 1, Assignment 2 Exercises
convolutional neural nets EC Ch 3 Assignment 2, Assignment 3
word embeddings and recurrent neural nets EC Ch 4 Assignment 4
sequence-to-sequence learning EC Ch 5 Assignment 5
deep reinforcement learning EC Ch 6 Assignment 6
unsupervised neural-network models EC Ch 7 Assignment 7


Assignment 1

Assignment 2

Assignment 3

Assignment 4

from nltk.corpus import treebank  as tb # PTB
from nltk import FreqDist # to determine word frequency
import numpy as np # for numpy arrays, etc.

frequency_list = FreqDist(i.lower() for i in words)
my_dictionary  = frequency_list.most_common()[:10000] # most common 10k
my_dictionary = np.array(my_dictionary) # convert to np array
my_dictionary = my_dictionary[:,0] # select words, ignore frequency
print(my_dictionary[:10]) # print the ten most frequent words

Homework, quiz, & exam policies

Homework & homework quiz policies

Weekly homework will be due on Fridays.

Working in groups on homework is strongly encouraged.

Grading policies

Total grades in the course may be curved, but individual assignments will not be. They will be available on moodle throughout the semester.



Academic integrity policy

Cheating or plagiarism of any kind is not tolerated and will result in a failing grade (“F”) in the course. I take this very seriously. Engineering is an academic and professional discipline that requires integrity. I expect students to consider their integrity of conduct to be their highest consideration with regard to the course material.