Explore time complexity in theoretical computer science through this comprehensive lecture from CMU's "Great Ideas in Theoretical Computer Science" course. Delve into the concept of running time, focusing on worst-case scenarios and the formal definition of O(n?) notation. Examine common run-time scaling patterns and their representation on log-log plots. Learn about intrinsic complexity and its implications for algorithm design. Gain insights from practical examples, including the analysis of a Palindrome Turing Machine. Understand the significance of time complexity in evaluating algorithm efficiency and performance across various input sizes.