Free machine learning introduction course by Ng (Stanford)

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Free machine learning introduction course by Ng (Stanford)

Post by siempre.aprendiendo » Sat Oct 15, 2011 7:23 pm

Post by siempre.aprendiendo
Sat Oct 15, 2011 7:23 pm

www.ml-class.org by Professor Andrew Ng and his team

It's, at least, as interesting as the more popular www.ai-class.com

The following is a tentative syllabus for the class:

Introduction to Machine Learning. Univariate linear regression. (Optional: Linear algebra review.)
Multivariate linear regression. Practical aspects of implementation. Octave tutorial.
Logistic regression, One-vs-all, Regularization.
Neural Networks, backpropagation, gradient checking.
Support Vector Machines (SVMs) and intuitions. Quick survey of other algorithms: Naive Bayes, Decision trees, Boosting.
Practical advice for applying learning algorithms: How to develop, debugging, feature/model design, setting up experiment structure.
Unsupervised learning: Agglomerative clustering, K-means, PCA, when to use each. (Optional/extra credit: ICA).
Anomaly detection. Combining supervised and unsupervised.
Other applications: Recommender systems. Learning to rank (search).
Large-scale/parallel machine learning and big data. ML system design/practical methods. Team design of ML systems.

www.ml-class.org by Professor Andrew Ng and his team

It's, at least, as interesting as the more popular www.ai-class.com

The following is a tentative syllabus for the class:

Introduction to Machine Learning. Univariate linear regression. (Optional: Linear algebra review.)
Multivariate linear regression. Practical aspects of implementation. Octave tutorial.
Logistic regression, One-vs-all, Regularization.
Neural Networks, backpropagation, gradient checking.
Support Vector Machines (SVMs) and intuitions. Quick survey of other algorithms: Naive Bayes, Decision trees, Boosting.
Practical advice for applying learning algorithms: How to develop, debugging, feature/model design, setting up experiment structure.
Unsupervised learning: Agglomerative clustering, K-means, PCA, when to use each. (Optional/extra credit: ICA).
Anomaly detection. Combining supervised and unsupervised.
Other applications: Recommender systems. Learning to rank (search).
Large-scale/parallel machine learning and big data. ML system design/practical methods. Team design of ML systems.

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