- 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.
Introduction to AI
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