Machine Learning Foundation (Math Emphasis) | Exploring Statistics, Algorithms and Neural Networks (TTML5504)

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations and practical application of these methods in a machine learning environment. This course reviews key foundational mathematics and introduces students to the algorithms of Data Science. Working in a hands-on learning environment, students will explore: Popular machine learning algorithms, their applicability and limitations Practical application of these methods in a machine learning environment Practical use cases and limitations of algorithms Core machine learning mathematics and statistics Supervised Learning vs. Unsupervised Learning Classification Algorithms including Support Vector Machines, Discriminant Analysis, Naïve Bayes, and Nearest Neighbor Regression Algorithms including Linear and Logistic Regression, Generalized Linear Modeling, Support Vector Regression, Decision Trees, k-Nearest Neighbors (KNN) Clustering Algorithms including k-Means, Fuzzy clustering, Gaussian Mixture Neural Networks including Hidden Markov (HMM), Recurrent (RNN) and Long-Short Term Memory (LSTM) Dimensionality Reduction, Single Value Decomposition (SVD), Principle Component Analysis (PCA) How to choose an algorithm for a given problem How to choose parameters and activation functions Ensemble methods