📄️ 32513 31005 Advanced Data Analytics Algorithms, Machine Learning
A guide to the available notes for 32513 31005 Advanced Data Analytics Algorithms, Machine Learning.
📄️ Module 1 - What Is Machine Learning?
An introduction to learning from data, statistical generalization, and when machine learning is useful.
📄️ Module 2 - Statistical Learning Framework
The data, hypothesis space, loss criterion, and algorithm that define a statistical learning system.
📄️ Module 3 - Learning Criterion and Loss Functions
Instance losses, information theory, structured prediction, expected and empirical risk, and Hoeffding's bound.
📄️ Module 4 - Hypothesis Design and Unsupervised Models
Hypothesis-space design through global and local basis functions, kernels, representation learning, and probabilistic generative models.
📄️ Module 5 - Training Algorithms and Gradient Optimization
Empirical-risk minimization, closed-form and data-based learners, gradient descent, learning-rate choices, and adaptive optimizers.