Neural Networks and Deep Learning Research

Neural Networks and Deep Learning Research

Columbia University course ECBM E6040

Zoran Kostic, Ph.D., Dipl. Ing., Professor of Professional Practice, zk2172(at)

Electrical Engineering Department, Data Sciences Institute, Columbia University in the City of New York

Course in a nutshell:

Advanced theory and practice of Deep Learning. Applications and projects.

Description: Advanced (second) course on Deep Learning

  • Bulletin Description: Regularized autoencoders, sparse coding and predictive sparse decomposition, denoising autoencoders, representation learning, manifold perspective on representation learning, structured probabilistic models for deep learning, Monte Carlo methods, training and evaluating models with intractable partition functions, restricted Boltzmann machines, approximate inference, deep belief networks, deep learning in speech and object recognition.


  • Spring 2020: Wednesdays 8:00am to 10:00 am

  • Spring 2019: Wednesdays 8:00am to 10:00 am

  • Spring 2018: Mondays 8:00am to 10:00 am.


  • Required prerequisites: knowledge of linear algebra, probability and statistics, programming, machine learning, first course in deep learning.

  • Prerequisite courses: ECBM E4040 or similar


  • Convolutional and recurrent neural networks.

  • Analytical study and software design.

  • Sevearal assignments in Python and in PyTorch

  • Significant project.

  • Pursuing deeper exploration of deep learning.


  • Lectures:

    • Presentation of material by instructors and guest lecturers

  • Student Presentations

    • Every student contributes several presentations on the subject of interest

  • Assignments:

    • Combination of analytical and programming assignments

  • Projects:

    • Team-based

    • Students with complementary backgrounds

    • Significant design

    • Reports and presentations to Columbia and NYC community

    • Best projects could qualify for publications and/or funding

Project Areas

  • Smart cities

  • Medical

  • Autonomous vehicles

  • Environmental

  • Physical data analytics

  • Finance

Books, Tools and Resources

2020 Spring Projects

  • Personality Detection

  • Edge Detection

  • DQN for game AI

  • Lane Detection

  • IOU, Lovasz-Softmax

  • Medical Imaging, Self-supervised, Object Detection

  • Training a DeeqQ Agent to control traffic lights

  • Future Vehicle Localization

2019 Projects

  • AlphaXENAS - Efficient neural architecture search by using reinforcement learning and MCTS

  • Quantitative Comparison of NLP Embeddings

  • Distruptive Optimization using Gaussian Gradients

  • Neural Segmentation of Podcast Content

  • Learning to generate captions for images

  • MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

  • Meta Learning

  • 3D Object tracking

  • Recurrent Neural Networks with Novel Regularization Mechanism

  • MRI Super-resolution

  • Semi-supervised Variational Autoencoder

2018 Projects

  • SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

  • Neural Image Caption Generation with Visual Attention

  • To Better Understanding of Arts with Deep learning

  • DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

  • Dialogue Generation with Reinforcement Learning

  • Dynamic Routing Between Capsules

  • Performance Profiling for Deep Learning

  • Generative Adversarial Text to Image Synthesis

  • Music Separation using Image Segmentation Networks (U-Net)

  • Real time object detection using Faster RCNN

  • A Neural Algorithm of Artistic Style

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.