AIsk (Ask about Artificial Intelligence) is a special project of mine to share some knowledge of AI to the Mandarinspeaking society from the popular science perspective. The knowledge will be presented in the form of short videos. For those interested in some deeper contents, you can check the MOOCs that I offered and the textbook Learning from Data: A Short Course that I coauthored.
The slides of AIsk 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..
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:

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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 11:31, August 02, 2021 Please feel free to contact me: 