4.3: Other Classifiers and Statistical Learning Methods
4.3.1: Kernel Methods
4.3.2: k-nearest Neighbor Algorithm
Make sure you know how the k-nearest neighbor algorithm works (in principle) after reading this entry.
4.3.3: Mixture Model
Read this article to learn about the different types of Mixture Models.
4.3.4: Naive Bayes Classifier
Read this article and make sure you know the definition of the naive Bayes classifier.
4.3.5: Decision Tree
Read this article. You should be able to define the term "decision tree" when you are done.
4.3.6: Kernels and Gaussian Processes
Watch the first video about machine learning and compare it to what you have learned already. After watching this video, you should the basics of linear regression, loss function, prediction techniques. Study non-linear models, probabilistic regression, and uncertainty estimation. Then, watch the second video lecture to learn about Bayesian regression and classification. Finally, watch the last lecture to learn about Gaussian processes, regression, and classification.