Machine Learning Basics

This lecture introduces the basics of machine learning for engineers, covering topics such as grading, course requirements, additional resources, and practical examples like predicting concrete strength, detecting defects in steel sheets, and understanding the concept of AI. It also delves into the history of AI, the data revolution, and examples of ML applications like GANs, text-to-image generation, and deep fakes. The road map of the course is outlined, including lectures on ML basics, deep learning, support vector machines, and clustering, with the ultimate goal of equipping students with the knowledge and tools to implement ML techniques independently.