Machine Learning Part 1: Supervised Learning and Neural Networks
A survey of machine learning topics including supervised, unsupervised, clustering and dimensionality reduction, and reinforcement learning
Machine Learning and Data Science topics
Machine Learning Part 2: Randomized Optimization
A practical look at Random Hill Climbing, Simulated Annealing, Genetic Algorithms, and MIMIC applied to neural network weight tuning and classic optimization problems.
Machine Learning Part 3: Unsupervised Learning & Dimensionality Reduction
Exploring clustering, Gaussian mixtures, and dimensionality reduction methods (PCA, ICA, RP, RF) on the Wine and Abalone datasets.