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  • Type MOOC course
  • Period 2019.08.30 ~ 2019.12.31
    17 weeks 5 days
  • hr Study freely
  • Course approval method Automatic approval

Instructor Introduction

Lecture plan

강의목록
  1. Week 01
    1. 1-1 Perceptron and its convergence theorem
    1. 1-2 Maximum Margin Principle and Soft Margin Hard Margin
    1. 1-3 Complexity of Linear Hypothesis and Margin Bound of Linear Classifiers
    1. 1-4 Linear models in Deep Neural Networks
  2. Week 02: Sparse modeling
    1. 2-1 Overview of sparse modeling
    1. 2-2 Matrix decomposition
    1. 2-3 Regularized likelihood methods
  3. Week 03: Causality
    1. 3-1 Introduction to Causality
    1. 3-2 Causal Bayesian Network
    1. 3-3 Temporal Causality
    1. 3-4 Counter-factual Inference
  4. Week 04: Explainable AI
    1. 4-1 Overview of Explainable AI
    1. 4-2 Explaining decision of deep learning
    1. 4-3 Explaining complex machine learning models by decomposition
    1. 4-4 Featured research projects in Explainable AI
  5. Week 05: Learning to learn
    1. 5-1 Meta learning
    1. 5-2 Few-shot learning
    1. 5-3 Model-Agnostic Meta-Learning
    1. 5-4 AutoML
  6. Week 06: Methods to Predict the Future Values
    1. 6-1 Time series prediction
    1. 6-2 Stationarity of time series
    1. 6-3 Non-stationary time series
    1. 6-4 Deep learning-based model and recent development
  7. Week 07: Reinforcement Learning: Self-Taught Artificial Intelligence
    1. 7-1 What is the reinforcement learning?
    1. 7-2 Approaches to reinforcement learning
    1. 7-3 xample: GridWorld
    1. 7-4 Example: CartPole