Lecture
This lecture provides an introduction to the course on machine learning, outlining its structure, objectives, and key concepts. The instructor discusses the organization of the course, including lecture schedules, exercise sessions, and evaluation methods. The importance of hands-on learning through pen-and-paper exercises is emphasized, as well as the coding exercises that will be conducted in Python. The lecture covers fundamental machine learning concepts, including supervised and unsupervised learning, and introduces linear regression as a primary focus. The instructor explains the significance of data in machine learning, detailing various types of data such as numerical, text, and images. The distinction between data samples and datasets is clarified, along with the insights that can be derived from machine learning models. The lecture concludes with a discussion on the importance of understanding the underlying algorithms and their applications in real-world scenarios, setting the stage for deeper exploration of machine learning techniques throughout the semester.