Introduction to Machine Learning

This lecture introduces the concept of Machine Learning as the science of enabling computers to learn and improve autonomously by processing data. Topics covered include recognizing hand-written digits, labeled training sets, supervised classification, generic schemes, and feature vectors. The instructor explains the importance of labeled training sets, supervised learning, and the key assumptions in machine learning. The lecture also delves into topics like decision boundaries, training versus testing, linear models, and polynomial curve fitting. Practical applications such as spam detection, recommender systems, and face recognition are discussed, along with the challenges of unbalanced training sets and multi-class k-NN classification.

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