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.