There were two parts to Lecture #3.
The first looked at local search and constraint satisfaction, that is search where we only care about the goal, not the path to the goal.
The second looked at the basics of machine vision.
Notes are available in two formats:
The lecture covered material from Chapters 4, 6 and 24.
If you have the textbook, you should go through Sections 4.1 (local search) and Section 6.1 to 6.4 (constraint satisfaction).
Though the textbook has a chapter on vision, Chapter 24, and it covers a lot of interesting material, it diverges quite a lot from what I covered. So there is no particular need to read it (though Section 24.2.1, which talks about edge detection, would be good reading for anyone who wants to learn more about convolution).
Finally, here is a small program, written in C++ (well, C with some C++ library calls), which uses a genetic algorithm to search a large space of possible solutions.