CS 6890: Deep Learning
Spring 2020


Time and Location: Tue, Thu 1:30 – 2:50 pm, ARC 321
Instructor: Razvan Bunescu
Office: Stocker 341
Office Hours: Wed, Fri 3:00 – 4:00 pm, or by email appointment
Email: bunescu @ ohio edu

Textbook: There is no required textbook for this class. Slides and supplementary materials will be made available on the course website.

Supplemental deep learning resources:
  • Dive into Deep Learning by Zhang, Lipton, Li, and Smola. Amazon, 2019.
  • CS 231n: Convolutional Neural Networks for Visual Recognition @ Stanford, 2019.
  • CS 498: Introduction to Deep Learning Svetlana Lazebnik @ Illinois, 2018.
  • Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville. MIT Press, 2016.
  • Deep Learning course sequence @ Coursera.

  • Machine learning introductions:
  • Machine Learning @ Ohio course website.
  • Machine Learning @ Coursera video lectures and exercises.
  • Machine Learning @ Stanford video lectures and exercises.

  • Course description:
    This course will introduce the multi-layer neural networks, a common deep learning architecture, and gradient-based training through the backpropagation algorithm. Fully connected neural networks will be followed by more specialized neural network architectures such as convolutional neural networks, recurrent neural networks with attention, and deep generative models. The later part of the course will explore more advanced topics, such as adversarial examples, deep reinforcement learning, and interpretability. The lectures will cover theoretical aspects of deep learning models, whereas homework assignments will give students the opportunity to build and experiment with shallow and deep learning models, for which skeleton code will be provided.

    Proposed topics:
    Logistic and Softmax Regression, Feed-Forward Neural Networks, Backpropagation, Vectorization, PCA and Whitening, Deep Networks, Convolution and Pooling, Recurrent Neural Networks, Long Short-Term Memory, Gated Recurrent Units, Neural Attention Models, Sequence-to-Sequence Models, Distributional Representations, Variational Auto-Encoders, Generative Adversarial Networks, Deep Reinforcement Learning.

    Prerequisites:
    Previous exposure to basic concepts in machine learning, such as: supervised vs. unsupervised learning, classification vs. regression, linear regression, logistic and softmax regression, cost functions, overfitting and regularization, gradient-based optimization. Experience with programming and familiarity with basic concepts in linear algebra and statistics.

    Lecture notes:
    1. Syllabus & Introduction
    2. Linear Regression, Logistic Regression, and Vectorization
    3. Gradient Descent algorithms
    4. Linear algebra and optimization in NumPy and PyTorch
    5. Feed-Forward Neural Networks and Backpropagation
    6. Unsupervised Feature Learning with Autoencoders
    7. Introduction to Automatic Differentiation, invited lecture by Dr. David Juedes.
    8. PCA, PCA whitening, and ZCA whitening
    9. Convolutional Neural Networks
    10. Word Embeddings
    11. Recurrent Neural Networks for NLP
    12. RNNs with Attention for Machine Translation

    Homework assignments1,2:
    1. Assignment and code.
    2. Assignment and code.
    3. Assignment, code and data.
    4. Assignment, code and data.
    5. Assignment, code, word2vec Google News embeddings, and the Stanford Natural Language Inference (SNLI) dataset.

    Paper Presentations
    Final Project:
    Online reading materials: