National Taiwan University
機器學習技法 (Machine Learning Techniques)
National Taiwan University

機器學習技法 (Machine Learning Techniques)

Taught in Chinese (Traditional)

5,989 already enrolled

Course

Gain insight into a topic and learn the fundamentals

林軒田

Instructor: 林軒田

4.9

(31 reviews)

Intermediate level
Some related experience required
18 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

4 quizzes

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There are 16 modules in this course

more robust linear classification solvable with quadratic programming

What's included

5 videos4 readings

another QP form of SVM with valuable geometric messages and almost no dependence on the dimension of transformation

What's included

4 videos

kernel as a shortcut to (transform + inner product): allowing a spectrum of models ranging from simple linear ones to infinite dimensional ones with margin control

What's included

4 videos

a new primal formulation that allows some penalized margin violations, which is equivalent to a dual formulation with upper-bounded variables

What's included

4 videos1 quiz

soft-classification by an SVM-like sparse model using two-level learning, or by a "kernelized" logistic regression model using representer theorem

What's included

4 videos

kernel ridge regression via ridge regression + representer theorem, or support vector regression via regularized tube error + Lagrange dual

What's included

4 videos

blending known diverse hypotheses uniformly, linearly, or even non-linearly; obtaining diverse hypotheses from bootstrapped data

What's included

4 videos

"optimal" re-weighting for diverse hypotheses and adaptive linear aggregation to boost weak algorithms

What's included

4 videos1 quiz

recursive branching (purification) for conditional aggregation of simple hypotheses

What's included

4 videos

bootstrap aggregation of randomized decision trees with automatic validation

What's included

4 videos

aggregating trees from functional + steepest gradient descent subject to any error measure

What's included

4 videos

automatic feature extraction from layers of neurons with the back-propagation technique for stochastic gradient descent

What's included

4 videos1 quiz

an early and simple deep learning model that pre-trains with denoising autoencoder and fine-tunes with back-propagation

What's included

4 videos

linear aggregation of distance-based similarities to prototypes found by clustering

What's included

4 videos

linear models of items on extracted user features (or vice versa) jointly optimized with stochastic gradient descent for recommender systems

What's included

4 videos

summary from the angles of feature exploitation, error optimization, and overfitting elimination towards practical use cases of machine learning

What's included

4 videos1 quiz

Instructor

Instructor ratings
5.0 (8 ratings)
林軒田
National Taiwan University
3 Courses53,268 learners

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