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機器學習技法 (Machine Learning Techniques)
  • Level Professional
  • المدة
  • الطبع بواسطة National Taiwan University
  • Offered by

عن

The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

الوحدات

Course Information

    • 1 Videos
    • 4 Readings
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1 Videos

  • Course Introduction

4 Readings

  • NTU MOOC 課程問題詢問與回報機制
  • 課程大綱
  • 延伸閱讀
  • 課程形式及評分標準

Lectures

    • 4 Videos
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4 Videos

  • Large-Margin Separating Hyperplane
  • Standard Large-Margin Problem
  • Support Vector Machine
  • Reasons behind Large-Margin Hyperplane

Lectures

    • 4 Videos
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4 Videos

  • Motivation of Dual SVM
  • Lagrange Dual SVM
  • Solving Dual SVM
  • Messages behind Dual SVM

Lectures

    • 4 Videos
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4 Videos

  • Kernel Trick
  • Polynomial Kernel
  • Gaussian Kernel
  • Comparison of Kernels

Lectures

    • 4 Videos
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4 Videos

  • Motivation and Primal Problem
  • Dual Problem
  • Messages behind Soft-Margin SVM
  • Model Selection

Homework

    • 1 Assignment
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1 Assignment

  • 作業一

Lectures

    • 4 Videos
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4 Videos

  • Soft-Margin SVM as Regularized Model
  • SVM versus Logistic Regression
  • SVM for Soft Binary Classification
  • Kernel Logistic Regression

Lectures

    • 4 Videos
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4 Videos

  • Kernel Ridge Regression
  • Support Vector Regression Primal
  • Support Vector Regression Dual
  • Summary of Kernel Models

Lectures

    • 4 Videos
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4 Videos

  • Motivation of Aggregation
  • Uniform Blending
  • Linear and Any Blending
  • Bagging (Bootstrap Aggregation)

Lectures

    • 4 Videos
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4 Videos

  • Motivation of Boosting
  • Diversity by Re-weighting
  • Adaptive Boosting Algorithm
  • Adaptive Boosting in Action

Homework

    • 1 Assignment
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1 Assignment

  • 作業二

Lectures

    • 4 Videos
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4 Videos

  • Decision Tree Hypothesis
  • Decision Tree Algorithm
  • Decision Tree Heuristics in C&RT
  • Decision Tree in Action

Lectures

    • 4 Videos
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4 Videos

  • Random Forest Algorithm
  • Out-Of-Bag Estimate
  • Feature Selection
  • Random Forest in Action

Lectures

    • 4 Videos
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4 Videos

  • Adaptive Boosted Decision Tree
  • Optimization View of AdaBoost
  • Gradient Boosting
  • Summary of Aggregation Models

Lectures

    • 4 Videos
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4 Videos

  • Motivation
  • Neural Network Hypothesis
  • Neural Network Learning
  • Optimization and Regularization

Homework

    • 1 Assignment
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1 Assignment

  • 作業三

Lectures

    • 4 Videos
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4 Videos

  • Deep Neural Network
  • Autoencoder
  • Denoising Autoencoder
  • Principal Component Analysis

Lectures

    • 4 Videos
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4 Videos

  • RBF Network Hypothesis
  • RBF Network Learning
  • k-Means Algorithm
  • k-Means and RBF Network in Action

Lectures

    • 4 Videos
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4 Videos

  • Linear Network Hypothesis
  • Basic Matrix Factorization
  • Stochastic Gradient Descent
  • Summary of Extraction Models

Lectures

    • 4 Videos
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4 Videos

  • Feature Exploitation Techniques
  • Error Optimization Techniques
  • Overfitting Elimination Techniques
  • Machine Learning in Practice

Homework

    • 1 Assignment
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1 Assignment

  • 作業四

Auto Summary

Enhance your data science expertise with the "Machine Learning Techniques" course, designed to expand the foundational knowledge from the "Machine Learning Foundations" course into practical and advanced models. This course, offered by Coursera, delves into three key areas: embedding numerous features, combining predictive features, and distilling hidden features. Ideal for professional-level learners, this course offers two subscription options, Starter and Professional, catering to varying levels of commitment and depth. Join this course to master robust machine learning tools and elevate your understanding of data science and AI.

林軒田
Instructor

林軒田