ECE 5973961/983: Artificial Neural Networks and Applications
Artificial neural networks was introduced in the 50’s of the last century. However, in the last decade, there has been strong resurgence of neural networks as processing techniques where they have been applied to many realworld problems. This leads to numerous breakthroughs on image, video, and natural language processing applications.
This course is aimed to be quite handson and should provide students with sufficient details for them to quickly apply to their own research. In particular, applications relating to computer vision and natural language processing will be discussed. There may be some math but we will not spend too much time going into proofs. Instead, we may try to go through (not exhaustively) some of the free libraries such as Caffe and Torch. And you are definitely encouraged to explore and leverage them for your course project.
Textbook
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press.
It is not required but is a very good reference.
Piazza
Reference
Some Deep Learning Toolboxes and Libraries
Tensorflow: From Google, probably most popular package. Not quite optimized for single PC
Caffe2: From Facebook
Caffe: From Berkeley
Torch 7: From NYU, and used by Facebook/Twitter
PyTorch: The Python version of Torch
Theano: From Bengio's group in Montreal
Keras: Highlevel layer on top of Theano/Tensorflow
Lasagne: Highlevel layer on top of Theano
matconvnet: From Oxford, kind of restricted
mxnet: From Amazon
Neon: From Intel
Deeplearning4j
Office Hours
There are no “regular” office hours. And you are welcome to come catch me anytime or contact me through emails.
Course Syllabus (Tentative)
Overview of machine learning
History of artificial neural networks
Perceptrons
Backpropagation algorithms
Regularization and dropout
Weight initialization
Optimization methods
Convolutional neural networks (CNN)
RCNN, faster RCNN
Weight visualization, Deep visualization, tSNE, deepdream
Recurrent neural networks
LSTM networks
Restricted boltzmann machines
Autoencoders
Deep belief networks
Projects
Video presentation due on May 8. Please read this for guideline. Written report is not mandatory but worth a maximum 20% extra credit.
Grading
"Activities": 30%. Quizzes, paper review, presentations, etc.
Homework: 30%. Programming assignments.
Final Project: 40%.
Final grade:
A: above 80%
B: above 60% but not more than 80%
C: above 40% but not more than 60%
D: above 20% but not more than 40%
F: not more than 20%
Prerequiste
Calculus (MATH 1914 or equivalent), linear algebra (MATH 3333 or equivalent), basic probability (MATH 4733 or equivalent), and intermediate programming skill (experience on Python/Numpy is preferred)
Note that we will “borrow” programming assignment from Stanford 231n. So ability to program in Python is expected. Python is not difficult if you are familiar with any other high level general programming languages such as C/C++/C#/Java/Javascript/Perl/Matlab etc. If you don't know anything about Python, I would recommend you to try out this app.
Late Policy
Reasonable Accommodation Policy
Any student in this course who has a disability that may prevent the full demonstration of his or her abilities should contact me personally as soon as possible so we can discuss accommodations necessary to ensure full participation and facilitate your educational opportunities.
Should you need modifications or adjustments to your course requirements because of documented pregnancyrelated or childbirthrelated issues, please contact me as soon as possible to discuss. Generally, modifications will be made where medically necessary and similar in scope to accommodations based on temporary disability. Please see this for commonly asked questions.
Calendar
 Topics  Materials  Further reading/watching 
1/16  Overview, AI, machine learning and its types, artificial neural networks and its history  overview  Andrew Ng: Artificial Intelligence is the New Electricity 
1/18  Machine learning basics  classification and regression  
1/23  Linear regression, ridge regression, Lasso (video)   regression notebook 
1/25  Linear classification, logistic regression, softmax, SVM (video)   
1/30  Neural networks, backpropagation (video)  neural networks  
2/1  Activation functions, weight initialization (video)   
2/6  Dropout, batch normalization, data augmentation, optimizers (video)   optimizer comparison notebook 
2/8  BFGS, LBFGS, babbysitting learning process (video)   
2/13  Convolutional neural networks (screencast was not captured successfully, please check this instead)  CNN  
2/15  CNN Architectures (video), presentation and final project (video)   
2/20  Class cancelled due to weather condition   
2/22  Class cancelled due to weather condition   
2/27  Torch (Hyeri's slides, Betsy's slides)   
3/1  Tensorflow   
3/6  Tensorflow   
3/8  Matconvnet   
3/13  visualizing CNN layers (video)  Visualizing CNN  
3/15  CNNs and arts, fooling CNN, semantic segmentation, object detection, YOLO (video)  CNN applications  
3/20  spring break   
3/22  spring break   
3/27  Mxnet (slides), caffe (slides)   
3/29  Keras (slides)   
4/3  RCNN (video)   
4/5  Recurrent neural networks (RNNs) , image captioning, long shortterm memory (LSTM) (video)  RNN  
4/10  Generative models, PixelCNN, PixelRNN (video)  Generative networks  
4/12  Autoencoder, generative adversarial network (GAN) (video)   
4/17  Variational autoencoder, neural machine translations, chatbots  Seq2seq models  
4/19  Neural Turng machine  Neural Turing machine  
4/24  Deep reinforcement learning  Deep reinforcement learning  AlphaGo Zero

