Stanford’s Free Online Instruction

Machine Learning

Tentative Syllabus:

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

Introduction to AI

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