Neural Networks and Deep Learning
Columbia University course ECBM E4040
Zoran Kostic, Ph.D., Dipl. Ing., Associate Professor of Professional Practice, zk2172(at)
Electrical Engineering Department, Columbia University in the City of New York

Course in a nutshell: 

    Theoretical underpinnings and practical aspects of Neural Networks and Deep Learning. Convolutional and Recurrent Neural Networks. Focus on applications and projects.

Bulletin Description:
  • Developing features & internal representations of the world, artificial neural networks, classifying handwritten digits with logistics regression, feedforward deep networks, back propagation in multilayer perceptrons, regularization of deep or distributed models, optimization for training deep models, convolutional neural networks, recurrent and recursive neural networks, deep learning in speech and object recognition.
  • Fall 2019: Fridays 10:10-12:20
  • Fall 2018: Fridays 10:10-12:20 (1) Course webpage(2) Syllabus
  • Fall 2017: Fridays 10:10-12:20.
  • Fall 2016: Fridays 10:10-12:20.
  • Required prerequisites: knowledge of linear algebra, probability and statistics, programming. Strongly recommended: machine learning.
  • Possible prerequisite courses: (BMEB W4020) or (BMEE E4030) or (ECBM E4090) or (EECS E4750) or (COMS W4771) or similar.

  • Introduction to neural networks.
  • Convolutional and recurrent neural networks.
  • Focuses on the intuitive understanding of deep learning.
  • Review of underpinning theory - linear algebra, statistics, machine learning.
  • Analytical study and software design.
  • Three-four assignments in Python and one DL framework (Tensorflow or PyTorch)
  • Significant project.
  • Enables further exploration of key concepts in deep learning.
  • Lectures:
    • Presentation of material by instructors and guest lecturers
  • Homeworks:
    • Combination of analytical and programming assignments
  • Projects:
    • Team-based
    • Students with complementary backgrounds
    • Significant design
    • Reports and presentations to Columbia and NYC community
    • Best could qualify for publications and/or funding
  • Industry participation:
    • Project definition and sponsoring
    • Weekly presentations
    • Interaction with students through mentoring
Project Areas
  • Medical
  • Autonomous cars
  • Environmental
  • Smart cities
  • Physical data analytics
 Books, Tools and Resources
2019 Projects
  • TBD
2018 Projects
  • A deep learning framework for relationship extraction from articles using long-short term memory and named entity recognition
  • A Neural Algorithm of Artistic Style
  • A Neural Representation of Sketch Drawings
  • Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
  • Backprop KF: Learning Discriminative Deterministic State Estimators
  • Deep contextualized word representations
  • Dynamic Routing Between Capsules
  • Gesture Recognition
  • Learned in Translation: Contextualized Word Vectors
  • Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
  • Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
  • Multi-Digit Number Recognition from Street View Imagery Using Deep Convolutional Neural Networks
  • Neural Networks for Automated Essay Grading
  • Parallel Multi-Dimensional LSTM,With Application to Fast Biomedical Volumetric Image Segmentation
  • PixelGAN Autoencoders
  • Prevention of catastrophic forgetting in Neural Networks for lifelong learning
  • Semantic Image Inpainting with Deep Generative Models
  • Towards Accurate Binary Convolutional Neural Network
  • Universal Style Transfer via Feature Transforms
  • Unsupervised Image-to-Image Translation Networks
2016 Projects
  • Striving for Simplicity: The All Convolutional Net
  • A Combined Semi-supervised Learning mechanism for Video Data via Deep Learning
  • A Neural Algorithm of Artistic Style
  • Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network
  • Colorful Image Colorization
  • Deep Networks with Stochastic Depth
  • Highway Networks
  • Image Super-Resolution Using Deep Convolutional Networks
  • Learning to Protect Communications with Adversarial Neural Cryptography
  • Singing Voice Separation from Monaural Recordings Using Deep
  • Recurrent Neural Networks
  • Spatial Transformer Networks
  • Spoken Language Understanding Using Long-Short Term Memory Neural Networks
  • Striving for Simplicity: The All Convolutional Net
  • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
2017 Projects
  • A Neural Algorithm of Artistic Style
  • BinaryConnect: Training Deep Neural Networks with Binary Weights during Propagations
  • Composing music with recurrent neural networks
  • Deep Learning Face Representation from Predicting 10,000 Classes
  • Deep Learning in Finance; Deep Portfolio Theory
  • Deep Networks with Stochastic Depth
  • DeepDriving: Deep Learning for Autonomous Driving
  • Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
  • Draw: A Recurrent Neural Network for Image Generation
  • Long Short Term Memory Networks for Anomaly Detection in Time Series
  • Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
  • Predicting HIV Risk Factors From Unstructured Clinical Text
  • Richer Convolutional Features for Edge Detection
  • Spectral Representations for Convolutional Neural Networks
  • Understanding Deep Learning Requires Rethinking Generalization
  • Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale

Course sponsored by equipment and financial contributions of:
  • NVidia GPU Education Center, Google Cloud, IBM Bluemix, AWS Educate, Atmel, Broadcom (Wiced platform); Intel (Edison IoT platform), Silicon Labs.