MLT | Week-1 | Session-1

In this first session of the Machine Learning Techniques course, instructor Karthik Thiagarajan introduces the course structure and fundamental concepts of machine learning. He begins by engaging the audience with polls and questions to gauge their background. He emphasizes that machine learning is essentially ’learning from data’ and outlines the course roadmap: the first four weeks cover unsupervised learning (PCA, clustering, distribution fitting), followed by two weeks on regression, and six weeks on classification. He explains the concept of a dataset using a housing price example, defining data points and features. He highlights that tabular data is the focus of this course, and introduces the data matrix representation, stressing the importance of linear algebra as a pillar of machine learning. He also mentions other data types like images, text, and time series, but clarifies that the course will concentrate on tabular data. The session sets expectations for the course’s difficulty and encourages students to watch the recorded lectures by Professor Arun. Overall, it serves as a foundational orientation for students.