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  • Type MOOC course
  • Period 2019.08.08 ~ 2019.12.31
    20 weeks 6 days
  • hr Study freely
  • Course approval method Automatic approval

Instructor Introduction

Lecture plan

강의목록
  1. Week 01: History of Artificial Intelligence and Machine Learning
    1. 1-1 Motivation and Turing Test - Can Machines Think?
    1. 1-2 Creation of Artificial Intelligence
    1. 1-3 Logical Artificial Intelligence - Expert System
    1. 1-4 Statistical Machine Learning
    1. 1-5 Neural Network and Deep Learning
    1. Quiz 1
  2. Week 02: Various Problems in Machine Learning
    1. 2-1 What are machine learning problems?
    1. 2-2 Learning with data and labels
    1. 2-3 Learning without labels
    1. Quiz 2
  3. Week 03: Finding Good Features to Solve Problems
    1. 3-1 What are good features and why are they important?
    1. 3-2 How to extract features automatically
    1. 3-3 What are composite features and why are they important?
    1. Quiz 3
  4. Week 04: Making Intelligence by Measuring the Similarity among Data Points
    1. 4-1 Distance Measures
    1. 4-2 Nearest Neighbor
    1. 4-3 Kernel Methods 1
    1. 4-4 Kernel Methods 2
  5. Week 05: Introduction to Neural Network
    1. 5-1 Artificial Neural Network
    1. 5-2 Convolutional Neural Network
    1. 5-3 Recurrent Neural Network
  6. Week 06: Participating in AI challenges
    1. 6-1 What are AI challenges?
    1. 6-2 Guide to Kaggle
    1. 6-3 Data Processing
    1. 6-4 Digit Recognizer
    1. Quiz 6
  7. Week 07: Participating in AI challenges (Advanced)
    1. 7-1 Methods of Ensemble
    1. 7-2 Avito Demand Prediction Challenge
    1. 7-3 Data Science Bowl 2018 & Toxic Comments