Course Structure#
Module 4: Unsupervised Learning and Dimensionality Reduction (Weeks 7-9)#
Focus: Unsupervised learning techniques and reducing data complexity.
Topics include clustering, Gaussian Mixture Models, PCA, and t-SNE.
Week 7: Ensemble Methods#
Lesson 31: Introduction to Ensemble Learning Techniques
Overview of ensemble learning and its importance in machine learning.
Math Focus: Concept of model combination and weighted averaging.
Lesson 32: Bagging and Random Forests
Understanding and implementing bagging and random forest algorithms.
Math Focus: Bootstrap sampling and its role in bagging.
Lesson 33: Boosting Algorithms - AdaBoost
Learning the AdaBoost algorithm and its implementation.
Math Focus: Boosting algorithms and weight updates.
Lesson 34: Gradient Boosting Machines (GBM) and XGBoost
Implementing GBM and XGBoost algorithms.
Math Focus: Gradient descent in boosting and regularization techniques in XGBoost.
Lesson 35: Advanced Ensemble Techniques and Their Applications
Exploring advanced concepts in ensemble learning.
Math Focus: Stacking, blending, and their mathematical principles.