인공지능255 [edwith] 인공지능 및 기계학습 개론 I : C5. Support Vector Machine - 문일철교수 [LECTURE] 5.1. Decision Boundary with Margin : edwith - 신승재 www.edwith.org [LECTURE] 5.2. Maximizing the Margin : edwith - 신승재 www.edwith.org [LECTURE] 5.3. SVM with Matlab : edwith - 신승재 www.edwith.org [LECTURE] 5.4. Error Handling in SVM : edwith - 신승재 www.edwith.org [LECTURE] 5.5. Soft Margin with SVM : edwith - 신승재 www.edwith.org [LECTURE] 5.6. Rethinking of SVM : edwith - 신승재 www.edwith.org.. 2021. 3. 25. [edwith] 인공지능 및 기계학습 개론 I : C4. Logistic Regression - 문일철교수 [LECTURE] 4.1. Decision Boundary : edwith - 신승재 www.edwith.org [LECTURE] 4.2. Introduction to Logistic Regression : edwith - 신승재 www.edwith.org [LECTURE] 4.3. Logistic Regression Parameter Approximation 1 : edwith - 신승재 www.edwith.org [LECTURE] 4.4. Gradient Method : edwith - 신승재 www.edwith.org [LECTURE] 4.5. How Gradient method works : edwith - 신승재 www.edwith.org [LECTURE] 4.6. Logistic Regress.. 2021. 3. 24. [edwith] 인공지능 및 기계학습 개론 I : C3. Naive Bayes Classifier - 문일철교수 [LECTURE] 3.1. Optimal Classification : edwith - 신승재 www.edwith.org [LECTURE] 3.2. Conditional Independence : edwith - 신승재 www.edwith.org [LECTURE] 3.3. Naive Bayes Classifier : edwith - 신승재 www.edwith.org [LECTURE] 3.4. Naive Bayes Classifier Application (Matlab Code) : edwith - 신승재 www.edwith.org - 출처: [edwith] 인공지능 및 기계학습 개론 I : C3. Naive Bayes Classifier - 문일철교수 2021. 3. 24. [edwith] 인공지능 및 기계학습 개론 I : C2. Fundamentals of Machine Learning - 문일철교수 [LECTURE] 2.1. Rule Based Machine Learning Overview : edwith - 신승재 www.edwith.org [LECTURE] 2.2. Introduction to Rule Based Algorithm : edwith - 신승재 www.edwith.org [LECTURE] 2.3. Introduction to Decision Tree : edwith - 신승재 www.edwith.org [LECTURE] 2.4. Entropy and Information Gain : edwith - 신승재 www.edwith.org [LECTURE] 2.5. How to create a decision tree given a training dataset : edwith - 신승재 .. 2021. 3. 23. 이전 1 2 3 4 5 6 ··· 64 다음
