Projects and Laboratories
1- Intro, Linear Regression
2- Maximum Likelihood, Logistic Regression,Clustering
3- K-Means, Gaussian Mixtures
4- Kernel Density, Support Vector Machines
5- Multi-class, Decision Trees, Random Forests
6- Bootstrap, Bagging, Boosting, Ensembles
7- Loss, Cross Validation, Hyperparameter Search
8- Neural Networks, Backpropagation
9- Convolutional Networks, Recurrent Networks
10- Dimensionality Reduction, PCA, Autoencoders
11- Generative Models, Naive Bayes, GaussianProcesses
12- Variational Autoencoders, Generative AdversarialNetworks
reference: Pattern Recognition and Machine Learning by Christopher M. Bishop (2006)
