I am fortunate to be among the very first NTU EECS professors to offer two Mandarin-teaching 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 co-authored. 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 CC-BY-NC 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:
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handout slides |
Lecture 2 | Data Structure:
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handout slides |
Lecture 3 | Asymptotic Notation:
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handout slides |
When can machines learn? | ||
Lecture 1 | the learning problem:
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handout slides; presentation slides |
Lecture 2 | learning to answer yes/no:
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handout slides; presentation slides |
Lecture 3 | types of learning:
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handout slides; presentation slides |
Lecture 4 | feasibility of learning:
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handout slides; presentation slides |
Why can machines learn? | ||
Lecture 5 | training versus testing:
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handout slides; presentation slides |
Lecture 6 | theory of generalization:
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handout slides; presentation slides |
Lecture 7 | the VC dimension:
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handout slides; presentation slides |
Lecture 8 | noise and error:
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handout slides; presentation slides |
How can machines learn? | ||
Lecture 9 | linear regression:
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handout slides; presentation slides |
Lecture 10 | logistic regression:
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handout slides; presentation slides |
Lecture 11 | linear models for classification:
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handout slides; presentation slides |
Lecture 12 | nonlinear transformation:
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handout slides; presentation slides |
How can machines learn better? | ||
Lecture 13 | hazard of overfitting:
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handout slides; presentation slides |
Lecture 14 | regularization:
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handout slides; presentation slides |
Lecture 15 | validation:
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handout slides; presentation slides |
Lecture 16 | three learning principles:
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handout slides; presentation slides |
embedding numerous features | ||
Lecture 1 |
linear support vector machine:
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handout slides; presentation slides |
Lecture 2 | dual support vector machine:
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handout slides; presentation slides |
Lecture 3 | kernel support vector machine:
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handout slides; presentation slides |
Lecture 4 | soft-margin support vector machine:
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handout slides; presentation slides |
Lecture 5 | kernel logistic regression:
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handout slides; presentation slides |
Lecture 6 | support vector regression:
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handout slides; presentation slides |
combining predictive features | ||
Lecture 7 | blending and bagging:
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handout slides; presentation slides |
Lecture 8 | adaptive boosting:
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Lecture 9 | decision tree:
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handout slides; presentation slides |
Lecture 10 | random forest:
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handout slides; presentation slides |
Lecture 11 | gradient boosted decision tree:
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handout slides; presentation slides |
distilling hidden features | ||
Lecture 12 | neural network:
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handout slides; presentation slides |
Lecture 13 | deep learning:
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handout slides; presentation slides |
Lecture 14 | radial basis function network:
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handout slides; presentation slides |
Lecture 15 | matrix factorization:
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handout slides; presentation slides |
happy learning! | ||
Lecture 16 | finale:
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handout slides; presentation slides |
Last updated at CST 15:34, August 30, 2024 Please feel free to contact me: ![]() |
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