Lectures related to Interpretability

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.