EE 514 (CS 535) Machine LearningSpring 2021
Syed Babar Ali School of Science and Engineering
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Introduction
Regression
ML Pipeline
Classification
Statistical Decision Theory
Linear Regression
Non-linear Regression
Bias-variance tradeoff
Linear Classification
Indicator Regression
Dimensionality Reduction, PCA, LDA
Naive Bayes
Logistic Regression
Perceptron
SVM
Decision Trees
Bagging, boosting, stacking
Neural Networks, Backpropagation
Training Deep Neural Networks
Convolutional neural networks intro
Recurrent Neural Networks
ML and MAP Estimation Theory
Bayesian Learning and Bayesian Linear Regression
Kernel Methods and Gaussian Process
K-means Clustering
Computational Learning Theory (Time Permitting)