Machine Learning: Supervised and Unsupervised Learning Techniques

This lecture introduces the fundamental concepts of machine learning, focusing on supervised and unsupervised learning techniques. It begins with an overview of supervised learning, explaining how algorithms learn from training data to predict outcomes. The instructor discusses the importance of regression and classification, providing examples of each. A case study illustrates the application of supervised learning in measuring carbon sequestration using satellite images and deep learning models. The lecture then transitions to unsupervised learning, highlighting its role in clustering and dimensionality reduction. The instructor explains how unsupervised learning can help identify patterns in large datasets without predefined labels. Reinforcement learning is briefly introduced, emphasizing its application in algorithmic trading. The lecture concludes with a discussion on the relevance of machine learning in finance, particularly in forecasting and risk assessment, and the challenges posed by data quality and bias. Overall, the lecture provides a comprehensive overview of machine learning techniques and their practical applications in various fields.