Machine Learning: Unsupervised Learning and Clustering Techniques

This lecture focuses on unsupervised learning, specifically clustering methods in machine learning. The instructor begins by contrasting supervised learning with unsupervised learning, emphasizing that in unsupervised learning, only input data is provided without output labels. The lecture introduces clustering, explaining how it groups data points based on their proximity in a defined distance metric. Various clustering methods are discussed, including K-Means and DBSCAN, highlighting their characteristics, advantages, and limitations. The instructor explains the importance of distance metrics, such as Euclidean and cosine similarity, in determining cluster formation. The lecture also covers the challenges of clustering in high-dimensional spaces, known as the curse of dimensionality, and the need for robust methods to handle noise and outliers. Practical applications of clustering in data exploration, marketing, and data labeling are presented, along with techniques for selecting the optimal number of clusters. The session concludes with a discussion on the interpretability and usability of clustering methods, setting the stage for future lectures on dimensionality reduction and text analysis.