Jason Ku introduces the course and fellow instructors Eric Demaine and Justin Solomon.

• Jason Ku will lead the first lecture, followed by Eric Demaine and Justin Solomon for subsequent lectures.
• The course is titled 'Introduction to Algorithms'.

The course's primary goals are to solve computational problems and effectively communicate solutions.

• Students will learn to solve computational problems, prove correctness, argue efficiency, and communicate solutions.
• Writing will be a significant part of the course, emphasizing the communication of ideas over coding.

A computational problem is defined, and the importance of problem specification is discussed.

• A computational problem is a binary relation between a set of inputs and a set of correct outputs.
• Problems are typically defined using predicates to check correctness, rather than enumerating all input-output pairs.

An algorithm is a function that maps inputs to correct outputs, and its correctness must be proven.

• An algorithm must output a correct result for each input, often verified using induction.
• The example of checking for matching birthdays in a class illustrates an algorithm and the concept of proving its correctness.

Efficiency is discussed in the context of comparing algorithm performance and using asymptotic analysis.

• Efficiency is not about actual time but the number of fundamental operations performed relative to input size.
• The class uses asymptotic notation such as big O, omega, and theta to describe algorithm efficiency.

A model of computation is established, and the structure of the course is outlined.

• The word RAM model is used for theoretical analysis of algorithms, with operations such as binary arithmetic and memory read/write in constant time.
• The course will cover data structures, graph algorithms, and dynamic programming across three quizzes.