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机器学习基石培训 台大讲师林轩田 机器学习基础入门培训视频教程,机器学习课程下载

木马童年 2020-6-9 19:24 107 0

课程名称机器学习基石培训 台大讲师林轩田 机器学习基础入门培训视频教程 机器学习课程课程目录01_handout.pdf02_handout.pdf03_handout.pdf04_handout.pdf05_handout.pdf06_handout.pdf07_handout.pdf08_handout.pd ...

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课程名称

机器学习基石培训 台大讲师林轩田 机器学习基础入门培训视频教程 机器学习课程

课程目录

  01_handout.pdf

  02_handout.pdf

  03_handout.pdf

  04_handout.pdf

  05_handout.pdf

  06_handout.pdf

  07_handout.pdf

  08_handout.pdf

  09_handout.pdf

  1 - 1 - Course Introduction (10-58).mp4

  1 - 2 - What is Machine Learning (18-28).mp4

  1 - 3 - Applications of Machine Learning (18-56).mp4

  1 - 4 - Components of Machine Learning (11-45).mp4

  1 - 5 - Machine Learning and Other Fields (10-21).mp4

  10 - 1 - Logistic Regression Problem (14-33).mp4

  10 - 2 - Logistic Regression Error (15-58).mp4

  10 - 3 - Gradient of Logistic Regression Error (15-38).mp4

  10 - 4 - Gradient Descent (19-18).mp4

  10_handout.pdf

  11 - 1 - Linear Models for Binary Classification (21-35).mp4

  11 - 2 - Stochastic Gradient Descent (11-39).mp4

  11 - 3 - Multiclass via Logistic Regression (14-18).mp4

  11 - 4 - Multiclass via Binary Classification (11-35).mp4

  11_handout.pdf

  12 - 1 - Quadratic Hypothesis (23-47).mp4

  12 - 2 - Nonlinear Transform (09-52).mp4

  12 - 3 - Price of Nonlinear Transform (15-37).mp4

  12 - 4 - Structured Hypothesis Sets (09-36).mp4

  12_handout.pdf

  2 - 1 - Perceptron Hypothesis Set (15-42).mp4

  2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4

  2 - 3 - Guarantee of PLA (12-37).mp4

  2 - 4 - Non-Separable Data (12-55).mp4

  3 - 1 - Learning with Different Output Space (17-26).mp4

  3 - 2 - Learning with Different Data Label (18-12).mp4

  3 - 3 - Learning with Different Protocol (11-09).mp4

  3 - 4 - Learning with Different Input Space (14-13).mp4

  4 - 1 - Learning is Impossible- (13-32).mp4

  4 - 2 - Probability to the Rescue (11-33).mp4

  4 - 3 - Connection to Learning (16-46).mp4

  4 - 4 - Connection to Real Learning (18-06).mp4

  5 - 1 - Recap and Preview (13-44).mp4

  5 - 2 - Effective Number of Lines (15-26).mp4

  5 - 3 - Effective Number of Hypotheses (16-17).mp4

  5 - 4 - Break Point (07-44).mp4

  6 - 1 - Restriction of Break Point (14-18).mp4

  6 - 2 - Bounding Function- Basic Cases (06-56).mp4

  6 - 3 - Bounding Function- Inductive Cases (14-47).mp4

  6 - 4 - A Pictorial Proof (16-01).mp4

  7 - 1 - Definition of VC Dimension (13-10).mp4

  7 - 2 - VC Dimension of Perceptrons (13-27).mp4

  7 - 3 - Physical Intuition of VC Dimension (6-11).mp4

  7 - 4 - Interpreting VC Dimension (17-13).mp4

  8 - 1 - Noise and Probabilistic Target (17-01).mp4

  8 - 2 - Error Measure (15-10).mp4

  8 - 3 - Algorithmic Error Measure (13-46).mp4

  8 - 4 - Weighted Classification (16-54).mp4

  9 - 1 - Linear Regression Problem (10-08).mp4

  9 - 2 - Linear Regression Algorithm (20-03).mp4

  9 - 3 - Generalization Issue (20-34).mp4

  9 - 4 - Linear Regression for Binary Classification (11-23).mp4

  HomeWork1.doc

  homework2.docx

  homework3.docx

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