CSE711: Advanced Machine Learning

Introduction to the state of the art Machine Learning, Gradient Descent and Stochastic Gradient Descent Algorithm, Bias-Variance and Regularization; Tree-based advanced ML Algorithms: Random Forest, AdaBoost, Gradient Boosting; Advanced Linear and Non-linear Regression Methods: Regression tree, Multiple Linear Regression; Advanced Unsupervised Learning: Clustering (K-Means++, Fuzzy C-Means, K-Medoids, DBSCAN), Data Dimensionality Reduction (LDA, t-SNE, and UMAP); Advanced Supervised Learning Algorithms for Classification: Support Vector Machine, Extreme Gradient Boosting; Model Assessment and Performance Metrics: Classification, Clustering and Regression; Neural Network and Deep Learning Basics: Forward Propagation, Back Propagation, Convolutional Neural Network, and Recurrent Neural Network and Practical MLOps.

Course Objectives

The core objective of this course are to:
Introduce to the students the developments in machine learning algorithms with an emphasis on the fundamental ideas of supervised, unsupervised, and cutting-edge learning theories and algorithms
Learn the algorithms that serve as the foundation for several advanced machine learning approaches
Apply machine learning algorithm to various real-world issues

List of Books

1. To Be Added

Course Outcome