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National Taiwan University

機器學習技法 (Machine Learning Techniques)

National Taiwan University via Coursera


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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。]


  • 第一講:Linear Support Vector Machine
    • more robust linear classification solvable with quadratic programming
  • 第二講:Dual Support Vector Machine
    • another QP form of SVM with valuable geometric messages and almost no dependence on the dimension of transformation
  • 第三講:Kernel Support Vector Machine
    • kernel as a shortcut to (transform + inner product): allowing a spectrum of models ranging from simple linear ones to infinite dimensional ones with margin control
  • 第四講:Soft-Margin Support Vector Machine
    • a new primal formulation that allows some penalized margin violations, which is equivalent to a dual formulation with upper-bounded variables
  • 第五講:Kernel Logistic Regression
    • soft-classification by an SVM-like sparse model using two-level learning, or by a "kernelized" logistic regression model using representer theorem
  • 第六講:Support Vector Regression
    • kernel ridge regression via ridge regression + representer theorem, or support vector regression via regularized tube error + Lagrange dual
  • 第七講:Blending and Bagging
    • blending known diverse hypotheses uniformly, linearly, or even non-linearly; obtaining diverse hypotheses from bootstrapped data
  • 第八講:Adaptive Boosting
    • "optimal" re-weighting for diverse hypotheses and adaptive linear aggregation to boost weak algorithms
  • 第九講:Decision Tree
    • recursive branching (purification) for conditional aggregation of simple hypotheses
  • 第十講:Random Forest
    • bootstrap aggregation of randomized decision trees with automatic validation
  • 第十一講:Gradient Boosted Decision Tree
    • aggregating trees from functional + steepest gradient descent subject to any error measure
  • 第十二講:Neural Network
    • automatic feature extraction from layers of neurons with the back-propagation technique for stochastic gradient descent
  • 第十三講:Deep Learning
    • an early and simple deep learning model that pre-trains with denoising autoencoder and fine-tunes with back-propagation
  • 第十四講:Radial Basis Function Network
    • linear aggregation of distance-based similarities to prototypes found by clustering
  • 第十五講:Matrix Factorization
    • linear models of items on extracted user features (or vice versa) jointly optimized with stochastic gradient descent for recommender systems
  • 第十六講:Finale
    • summary from the angles of feature exploitation, error optimization, and overfitting elimination towards practical use cases of machine learning

Taught by

Hsuan-Tien Lin


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4.9 rating at Coursera based on 31 ratings

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