Hi EE Majors -A brief overview of what topics are covered in each course EEE3773 – Types of learning, evaluation metrics and goodness-of-fit measures, k-nearest neighbors, decision trees, random forests, Bootstrap sampling, Linear Classifiers (LDA, Perceptron, logistic regression), feature selection and feature extraction, experimental design and hyperparameter tuning, neural networks (MLP, architectures), Deep Learning (CNNs), scikit-learn library, PyTorch, Git and GitHub. EEE4773 – Types of learning, Experimental Design, Curse of Dimensionality, Maximum Likelihood Estimation, Maximum A Posteriori, Conjugate priors, probabilistic generative models, Naïve Bayes classifier, Mixture Model (Gaussian Mixture Model), Expectation-Maximization (EM) algorithm, evaluation metrics and goodness-of-fit measures, k-means clustering, k-nearest neighbors, Discriminative classification (LDA, Perceptron, logistic regression), Lagrange multipliers, hard-margin and soft-margin SV...
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