Machine Learning Foundations+Machine Learning Techniques
1.Machine Learning Foundations
（機器學習基石）
Hsuan-Tien Lin（林軒田
Department of Computer Science
&Information Engineering
National Taiwan University
（國立台灣大學資訊工程系）

Course Design (1/2)
Machine Learning: a mixture of theoretical and practical tools theory oriented derive everything deeply for solid understanding less interesting to general audience techniques oriented flash over the sexiest techniques broadly for shiny coverage too many techniques, hard to choose, hard to use properly

课件：
Lecture 7 the VC dimension.pdf
Lecture 9 linear regression.pdf
README.md
Lecture 8 noise and error.pdf
Lecture 6 theory of generalization.pdf
Lecture 1 the learning problem.pdf
Lecture 3 types of learning.pdf
Lecture 4 feasibility of learning.pdf
Lecture 15 validation.pdf
Lecture 5 training versus testing.pdf
Lecture 2 learning to answer yes or no.pdf
Lecture 16 three learning principles.pdf
Lecture 10 logistic regression.pdf
Lecture 14 regularization.pdf
Lecture 11 linear models for classification.pdf
Lecture 12 nonlinear transformation.pdf
Lecture 13 hazard of overfitting.pdf




2.Machine Learning Techniques
（機器學習技法）


Course Design
from Foundations to Techniques
mixture of philosophical illustrations, key theory, core algorithms, usage in practice, and hopefully jokes :-)
three major techniques surrounding feature transforms: Embedding Numerous Features: how to exploit and regularize numerous features?
inspires Support Vector Machine (SVM) model Combining Predictive Features: how to construct and blend predictive features?
inspires Adaptive Boosting (AdaBoost) model Distilling Implicit Features: how to identify and learn implicit features?
—inspires Deep Learning model

课件：
Lecture 7 blending and bagging.pdf
Lecture 8 adaptive boosting.pdf
Lecture 9 decision tree.pdf
README.md
Lecture 3 kernel support vector machine.pdf
Lecture 4 soft-margin support vector machine.pdf
Lecture 6 support vector regression.pdf
Lecture 16 finale.pdf
Lecture 5 kernel logistic regression.pdf
Lecture 2 dual support vector machine.pdf
Lecture 15 matrix factorization.pdf
Lecture 11 gradient boosted decision tree.pdf
Lecture 14 radial basis function network.pdf
Lecture 10 random forest.pdf
Lecture 12 neural network.pdf
Lecture 13 deep learning.pdf
Lecture 1 linear support vector machine.pdf