강의목록 Help

CHAPTER 1. Motivations and Basics
- 강좌 수강을 환영합니다! 여기부터 꼭 보고 넘어가세요-!
  
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- 1.1. Motivations
  
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- 1.2. MLE
  
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- 1.3. MAP
  
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- 1.4. Probability and Distribution
  
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- Ch1. Quiz
  
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  2019.02.22
  
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CHAPTER 2. Fundamentals of Machine Learning
- 2.1. Rule Based Machine Learning Overview
  
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- 2.2. Introduction to Rule Based Algorithm
  
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- 2.3. Introduction to Decision Tree
  
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- 2.4. Entropy and Information Gain
  
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- 2.5. How to create a decision tree given a training dataset
  
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  2019.12.09
  
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- Ch2. Quiz
  
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  2017.04.13
  
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CHAPTER 3. Naive Bayes Classifier
- 3.1. Optimal Classification
  
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- 3.2. Conditional Independence
  
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- 3.3. Naive Bayes Classifier
  
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  2019.10.23
  
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- 3.4. Naive Bayes Classifier Application (Matlab Code)
  
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- Ch3. Quiz
  
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  2017.04.13
  
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CHAPTER 4. Logistic Regression
- 4.1. Decision Boundary
  
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- 4.2. Introduction to Logistic Regression
  
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- 4.3. Logistic Regression Parameter Approximation 1
  
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- 4.4. Gradient Method
  
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- 4.5. How Gradient method works
  
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- 4.6. Logistic Regression Parameter Approximation 2
  
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- 4.7. Naive Bayes to Logistic Regression
  
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- 4.8. Naive Bayes vs Logistic Regression
  
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- Ch4. Quiz
  
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CHAPTER 5. Support Vector Machine
- 5.1. Decision Boundary with Margin
  
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- 5.2. Maximizing the Margin
  
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- 5.3. SVM with Matlab
  
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- 5.4. Error Handling in SVM
  
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- 5.5. Soft Margin with SVM
  
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- 5.6. Rethinking of SVM
  
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- 5.7. Primal and Dual with KKT Condition
  
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- 5.8. Kernel
  
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- 5.9. SVM with Kernel
  
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- Ch5. Quiz
  
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CHAPTER 6. Training Testing and Regularization
- 6.1. Over-fitting and Under-fitting
  
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- 6.2. Bias and Variance
  
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- 6.3. Occam's Razor
  
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- 6.4. Cross Validation
  
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- 6.5. Performance Metrics
  
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- 6.6. Definition of Regularization
  
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- 6.7. Application of Regularization
  
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- Ch6. Quiz
  
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  2019.02.22
  
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★강의 수강 후 의견을 부탁드리겠습니다.★
- 교수님 강의에 대한 별점을 매겨주세요. 여러분의 의견이 많은 도움이 됩니다:D
  
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