I am fortunate to be among the very first NTU EECS professors to offer two Mandarinteaching MOOCs (massive open online courses) on NTU@Coursera. The two MOOCs are Machine Learning Foundations (Mathematical, Algorithmic) and Machine Learning Techniques and are based on the textbook Learning from Data: A Short Course that I coauthored. The book is consistently among the best sellers in Machine Learning on Amazon.
The slides of the MOOCs below are available as is with no explicit or implied warranties. The slides themselves are shared by CCBYNC 3.0, but the copyright of all materials (figures in particular) remain with the original copyright holder (in almost all cases the authors of the Learning from Data: A Short Course book).
Here are some quick links for each MOOC. The erratas are here.
Machine Learning Foundations  Mathematical Foundations MOOC Algorithmic Foundations MOOC 
all handout slides  free youtube videos  
Machine Learning Techniques  Techniques MOOC  all handout slides  free youtube videos 
Detailed outlines for each MOOC, along with the presentation sldies, are listed below.
Lecture 1  Algorithm:

handout slides 
Lecture 2  Data Structure:

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When can machines learn?  
Lecture 1  the learning problem:

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Lecture 2  learning to answer yes/no:

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Lecture 3  types of learning:

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Lecture 4  feasibility of learning:

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Why can machines learn?  
Lecture 5  training versus testing:

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Lecture 6  theory of generalization:

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Lecture 7  the VC dimension:

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Lecture 8  noise and error:

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How can machines learn?  
Lecture 9  linear regression:

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Lecture 10  logistic regression:

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Lecture 11  linear models for classification:

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Lecture 12  nonlinear transformation:

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How can machines learn better?  
Lecture 13  hazard of overfitting:

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Lecture 14  regularization:

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Lecture 15  validation:

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Lecture 16  three learning principles:

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embedding numerous features  
Lecture 1 
linear support vector machine:

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Lecture 2  dual support vector machine:

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Lecture 3  kernel support vector machine:

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Lecture 4  softmargin support vector machine:

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Lecture 5  kernel logistic regression:

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Lecture 6  support vector regression:

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combining predictive features  
Lecture 7  blending and bagging:

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Lecture 8  adaptive boosting:
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Lecture 9  decision tree:

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Lecture 10  random forest:

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Lecture 11  gradient boosted decision tree:

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distilling hidden features  
Lecture 12  neural network:

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Lecture 13  deep learning:

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Lecture 14  radial basis function network:

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Lecture 15  matrix factorization:

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happy learning!  
Lecture 16  finale:

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Last updated at CST 02:10, October 07, 2022 Please feel free to contact me: 