Logistics

Instructors:

TAs:

  • Jianlin Du (jianlind at andrew.cmu.edu). Office: GHC 5th Commons, Mondays 10:30 - 11:30 AM, now via Zoom
  • Yiyang Ge(yiyangge at andrew.cmu.du). Office: GHC 5th Commons, Mondays 2:00 - 3:00 PM, now via Zoom

Meetings: Tue and Thu 10:30 - 11:50 at GHC 4102, now via Zoom

    Piazza: https://piazza.com/cmu/spring2019/cs11695

    This is a hands-on implementation course focusing on building deep learning networks using Python and Tensorflow. You will develop the following skills:

    • Program supervised learning, un-supervised learning and reinforcement learning
    • Apply deep learning models, including feedforward neural networks, convolutional neural networks, recurrent neural networks, encoder-decoder and other probabilistic models
    • Construct computer vision, machine translation, control systems and other applications.

    TOPICS

    • Feed-forward Network
    • Convolutional Neural Network
    • Recurrent Neural Network, Sequence-to-Sequence and Machine Translation
    • Large-Scale Machine Learning
    • AutoEncoder, VAE, GAN, Normalizing Flows
    • Reinforcement Learning

    PREREQUISITES

    • Probability and Statistics: PDF, CDF, Basic Distributions, Basic Bayesian Statistics
    • Linear Algebra: Vector, Matrix, Eigen Value Decomposition, Eigen Vectors
    • Multivariate Calculus: Integrals, Gradients, Partial Derivaties, Hessians
    • Proficient Python

    Assessment

      1. There is NO mid-term or final exam. You will be graded based on weekly quizzes and 3 big assignments, as follows:
    • (25%) Quizzes: there will be an about 15-min test by the end of each lecture which will cover the knowledge of the previous one. There will be multiple-choice questions and in some cases, some short explanation is also expected. Three worst quizzes will be dropped.
    • (75%) Coding assignments: there are 3-4 assignments which require you to work individually. The code will be submitted to Canvas and will be graded individually.
      2. NOTE: You only have ONE WEEK to DISPUTE or make a regrade request for any quizz/homework. After that, the grade is unchangable.
      3. GRACE DAY: 5 days for HWs, you choose how to distribute them.
      • And after that, no more, i.e. ZERO otherwise.

    RESOURCES

      For textbooks, this course requires no official textbooks but we recommend you to read the following texts:
      Furthermore, the instructors and TAs will update useful links on Piazza to assist you more on learning the theories and implementation.
      For compute resources, we will provide all students enough Amazon AWS credits. The TAs will instruct you on how to use AWS and Google Colab resources which also provide you abundant free compute power for testing the codes.

    PLAGIARISM

      All work in this course, including both quizzes and assignments, must be your own individual work. That is, you must work alone on quizzes and assignments. If you include material from another source in any work, you MUST provide a citation to the source.
      Violations of the Academic Integrity Policy are taken very seriously and the instructors MUST report any violations as provided by policy.
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