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 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  MOOC  all handout slides  free youtube videos  
Machine Learning Techniques  MOOC  all handout slides  free youtube videos 
Detailed outlines for each MOOC, along with the presentation sldies, are listed below.
When can machines learn?  
Lecture 1  the learning problem:

handout slides; presentation slides 
Lecture 2  learning to answer yes/no:

handout slides; presentation slides 
Lecture 3  types of learning:

handout slides; presentation slides 
Lecture 4  feasibility of learning:

handout slides; presentation slides 
Why can machines learn?  
Lecture 5  training versus testing:

handout slides; presentation slides 
Lecture 6  theory of generalization:

handout slides; presentation slides 
Lecture 7  the VC dimension:

handout slides; presentation slides 
Lecture 8  noise and error:

handout slides; presentation slides 
How can machines learn?  
Lecture 9  linear regression:

handout slides; presentation slides 
Lecture 10  logistic regression:

handout slides; presentation slides 
Lecture 11  linear models for classification:

handout slides; presentation slides 
Lecture 12  nonlinear transformation:

handout slides; presentation slides 
How can machines learn better?  
Lecture 13  hazard of overfitting:

handout slides; presentation slides 
Lecture 14  regularization:

handout slides; presentation slides 
Lecture 15  validation:

handout slides; presentation slides 
Lecture 16  three learning principles:

handout slides; presentation slides 
embedding numerous features  
Lecture 1 
linear support vector machine:

handout slides; presentation slides 
Lecture 2  dual support vector machine:

handout slides; presentation slides 
Lecture 3  kernel support vector machine:

handout slides; presentation slides 
Lecture 4  softmargin support vector machine:

handout slides; presentation slides 
Lecture 5  kernel logistic regression:

handout slides; presentation slides 
Lecture 6  support vector regression:

handout slides; presentation slides 
combining predictive features  
Lecture 7  blending and bagging:

handout slides; presentation slides 
Lecture 8  adaptive boosting:
 handout slides; presentation slides 
Lecture 9  decision tree:

handout slides; presentation slides 
Lecture 10  random forest:

handout slides; presentation slides 
Lecture 11  gradient boosted decision tree:

handout slides; presentation slides 
distilling hidden features  
Lecture 12  neural network:

handout slides; presentation slides 
Lecture 13  deep learning:

handout slides; presentation slides 
Lecture 14  radial basis function network:

handout slides; presentation slides 
Lecture 15  matrix factorization:

handout slides; presentation slides 
happy learning!  
Lecture 16  finale:

handout slides; presentation slides 
Last updated at CST 15:20, December 26, 2016 Please feel free to contact me: 