Advanced Machine Learning

6-7 weeks

LearningRepresentation & Reasoning

Overview

Students explore supervised and unsupervised learning at an advanced level, working with real datasets and implementing algorithms from scratch. They learn to evaluate models rigorously, handle imbalanced data, and understand the mathematical foundations of ML.

Learning Objectives

1
Implement and train advanced supervised learning models using scikit-learn and custom Python
2
Apply dimensionality reduction techniques to high-dimensional datasets
3
Evaluate models using multiple metrics and cross-validation strategies
4
Handle class imbalance and data preprocessing challenges
5
Interpret feature importance and model predictions
6
Apply unsupervised learning for clustering and pattern discovery

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