Course 4541.676:
Dynamic Learning: Architectures and Algorithms (2010)

  • School of Computer Science and Engineering, Seoul National University
  • Instructor: Prof. Byoung-Tak Zhang
  • TA: Ha-Young Jang (Office : 138-409, Tel : 02-880-5890)
  • Classroom: 302-209
  • Time: Mon 11:00-12:15 and Wed 11:00-12:15
  • Course description:
    • Conventional machine learning models, including both supervised and unsupervised models, assume a training set of data items, all of which is available when learning starts. However, many real applications involve with dynamic learning situations where the training data come in a stream sequentially and continuously in a long-lasting process. Typically the target distribution of the learning is non-stationary and changing with uncertainty. Conventional learning methods are often insufficient to deal with this. Learning in dynamic environments requires different architectures and algorithms since the objective of learning should, for example, balance the tradeoff between long-term and short-term values. This course reviews the architectures and algorithms developed for dynamic learning settings, and discuss some application examples. A project will be assigned in which the students will develop a dynamical system that learns from video streams (i.e. sequences of picture-text pairs) to perform a visual storytelling.
  • Evaluation:
    • One exam (40%)
    • One miniprojects + Two reports (50%)
    • Participation in discussion (10%)
  • Project :
    • Visual storytelling : Learning to regenerate the sequence of image-text pairs (from video)
      - Description   Data   Notice! Data is changed!(09.17)
    • References

  • Lecture Schedule
  • Date



    9/1 Wed

    Course overview

    9/6 Mon

    Motivating examples
    Dynamical systems

    9/8 Wed

    Dynamic learning
    Visual storytelling project


    9/13 Mon

    Autoregressive models
    Time-series analysis
    AR, MA, ARMA
    Term project announced (First report due 10/13)


    9/15 Wed

    Kalman filters
    Model, algorithms, variants


    9/20 Mon

    No class (Thanksgiving holiday)

    9/22 Wed

    No class (Thanksgiving holiday)

    9/27 Mon

    Monte Carlo filtering
    Monte Carlo approximation
    Importance Sampling (IR)

    Supplement 1
    Supplement 2

    9/29 Wed

    Particle filters
    Sequential Monte Carlo (SMC)
    Vidio lecture :

    10/4 Mon

    Evolutionary Monte Carlo
    EC, PSO, evolutionary MCMC, EDA

    Supplement 1
    Supplement 2
    Supplement 3

    10/6 Wed

    Monte Carlo and the mind
    Video lecture:

    10/11 Mon

    Markov decision processes (MDPs)
    Definition, problem, solution
    Partially observable MDPs


    10/13 Wed

    Reinforcement learning
    Due of project report 1 is postponed for a week.


    10/18 Mon

    Video lecture: Reinforcement learning

    10/20 Wed

    Video lecture: Graphical models
    Due of porject report 1

    10/25 Mon

    Hidden Markov models (HMMS)
    Model, learning, inference


    10/27 Wed

    Conditional random fields (CRFs)
    Model, learning, inference


    11/1 Mon

    Dynamic Bayesian networks
    Architecture, learning, inference
    Video lecture: Dynamic factor graphs (DFGs)


    11/3 Wed

    Dynamic hypernetworks
    Architecture, learning, inference


    11/8 Mon


    11/10 Wed


    11/15 Mon

    No Class

    11/17 Wed


    11/22 Mon

    Project presentation 1
    Bado Lee
    Eun-Sol Kim and Myung-Goo Kang
    Byoung-Kwon Lim

    11/24 Wed

    Project presentation 2
    Taemin Park, Joonso Lee and Dooyoung Kim
    Minkyu Kim and Weerayot Aramphianlert

    11/29 Mon

    Project presentation 3

    12/1 Wed

    Future of dynamic learning

    12/6 Mon

    Due of project report 2

    This page is maintained by Eun Sol Kim 
    Last updated: Sep 27, 2010.