CS 677: Deep learning
Spring 2019

Instructor: Usman Roshan
Office: GITC 4214B
Ph: 973-596-2872
Office hours: TW: 2 to 5
TA: TBA
Email: usman@njit.edu

Textbook: Not required
Grading: 40% programming assignments, 25% mid-term, 35% final exam
Course Overview: This course will cover deep learning and current topics in data science. We will begin with machine learning background and then move to CUDA and OpenCL languages for parallel programming on Graphics Processing Units (GPUs) followed by OpenMP for multi-core programming. We will then proceed to neural networks, machine learning for image recognition, convolutional filters for image recognition, convolutional neural networks, optimization algorithms to train such networks, adversarial attacks, and deep learning for text.

Course plan:

Topic	Date	Notes
Basic machine learning and Python scikit-learn	01/23/2019	Introduction Basic Unix command sheet Instructions for AFS login Basic machine learning background with Python scikit-learn Datasets Linear data Non-linear data Breast cancer (bc.train.0) (bc.test.0) Ionosphere (ion.train.0) (ion.test.0) Climate simulation (climate.train.0) (climate.test.0) Qsar (qsar.train.0) (qsar.test.0) Fifty two datasets selected from the UCI machine learning repository
Introduction to GPU computing	01/28/2019	GPU coding (also see Cuda by Example by Kandrot and Sanders) Numpy tutorials Official Numpy tutorial External Numpy tutorial CUDA in Python Numba CUDAJIT in Anaconda PyCUDA (PyCUDA slides)
CUDA programming	01/30/2019	Parallel chi-square 2-df test Chi-square 2-df test in parallel on a GPU Simulated GWAS Class labels for above data
CUDA programming	02/04/2019	Parallel Chi-square 2-df test Assignment 1
OpenCL and OpenMP programming	02/06/2019	CUDA to OpenCL slides libOpenCL.so (NVIDIA library file for OpenCL code) Chi2 opencl implementation OpenCL files CUDA to OpenMP slides OpenMP reference Chi2 openmp implementation Assignment 2
Neural networks	02/11/2019	Multi-layer perceptrons Scikit-learn MLP code
Image classification	02/13/2019	Image classification code Python Image Library
Convolutional kernels for images	02/18/2019	Convolutional kernels
Convolutional neural networks	02/20/2019	Convolutional neural network (Additional slides by Yunzhe Xue) Convolution and single layer neural networks objective and optimization Flower image classification with CNNs code
Convolutional neural networks	02/25/2019	Convolution and single layer neural networks objective and optimization
Back propagation for single layer network with numpy	02/27/2019	Assignment 3
Online lecture for backpropagation weight updates	03/02/2019
NJIT closed due to weather	03/04/2019
Back propagation for single layer network with numpy, stochastic gradient descent	03/06/2019	Optimization in neural networks Assignment 4
Back propagation for convolutional network with one convolutional layer followed by global average pooling	03/11/2019	Assignment 5
Introduction to Keras	03/13/2019
Spring Break	03/18/2019
Spring Break	03/20/2019
Spring Break lecture	03/22/2019
Keras and mid-term review	03/25/2019	Mid-term review sheet Assignment 6
	03/27/2019	Mid-term exam
Convolutional neural networks in Keras: convolutional blocks and layering	04/01/2019	Image classification code v2
Convolutional neural networks in Keras: normalization types and their effect	04/03/2019	Batch normalization Batch normalization paper Group normalization paper How does batch normalization help optimization
Convolutional neural networks: gradient descent optimization. Large-scale image classification	04/08/2019	Gradient descent optimization An overview of gradient descent optimization algorithms Curriculum learning CIFAR 10 CIFAR 100 STL 10 Mini ImageNet
More on training convolutional neural networks	04/10/2019	On training deep networks The Loss Surfaces of Multilayer Networks Ant colony optimization for deep networks Simulated Annealing Algorithm for Deep Learning
Convolutional neural networks in Keras: pertrained models and transfer learning Convolutions and deconvolutions, temporal convolutional kernels Common architectures: ResNet, DenseNet, VGG	04/15/2019	A guide to convolution arithmetic for deep learning Common architectures Transfer learning by Yunzhe Xue Pre-trained models in Keras
Continuation of above plus cross-entropy loss function vs. least squares	04/17/2019	Assignment 7 Mini ImageNet in original form. Data are 256x256 images spread across different directories
Multi-path networks, data augmentation, time-series and sequence networks	04/22/2019	An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Image classification on Kaggle datasets, self-designed networks, self-supervised learning	04/24/2019	Assignment 8 Kaggle datasets: (a) Fruits (b) Flowers (c) Chest X-rays Exploring Randomly Wired Neural Networks for Image Recognition Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification UNSUPERVISED REPRESENTATION LEARNING BY PREDICTING IMAGE ROTATIONS Unsupervised Visual Representation Learning by Context Prediction Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Image retrieval, U-networks	04/29/2019	U-Net: Convolutional Networks for Biomedical Image Segmentation GitHub UNet in Keras
Image retrieval	05/01/2019	Deep Learning for Image Retrieval: What Works and What Doesn't CNN Features off-the-shelf: an Astounding Baseline for Recognition Deep Learning for Content-Based Image Retrieval: A Comprehensive Study
Adversarial attacks, final exam review	05/06/2019	Final exam review sheet