# Lecture 20 Examples

📗 My handwriting is really bad, you should copy down your notes from the lecture videos instead of using these.
Lecture 20 Zoom Annotated (2021): Link
Lecture 20 Pre-recorded Annotated (from 2020, please use with caution): Link

# Discussion Sessions

📗 The recorded videos of the session are on Canvas, the notes are not very useful but here they are anyways,
Week 6 Discussion Notes: Link

# Sharing Solutions on Piazza

📗 Use the sign-up sheet: Google Sheet
📗 You can sign up and post anonymously (anonymous Piazza posts are not anonymous to instructors).
📗 You must post before the official deadline of the homework and your post must include: (1) a copy or a screenshot of your version of the question and (2) detailed solution and explanation to how you come up with the solution.
📗 Each good post will receive 0.25 points. Incorrect solutions and/or solutions without explanations will receive no points.

# Q20 Discussion Topic

📗 Please create a follow-up discussion post on the Piazza (it is okay to post anonymously). No Canvas submissions are required. The grades will be updated at the end of the week on Canvas.
📗 The official deadline is August 1: if you post after this deadline, your Quiz grade on Canvas will not be updated until the midterm or the final exam. You can post and earn the points until August 10.
📗 Go to the PathFinding page: Link and try to "take two minutes to design a maze that takes one minute to solve" (not literally). Share the screenshot (with the solution to the maze) on Piazza: Link.
📗 You can try to replicate the one from Inception, but it's okay to have something less complicated.


Image from the Christopher Nolan movie "Inception" (2010) copyright Warner Bros..

# Flappy Bird Demo

This is a simple genetic algorithm applied to training a neural network to control a (poorly drawn) flappy bird. The inputs are horizontal and vertical distance to the next gap, and the output is binary whether the bird should flap. You can control the red bird by clicking anywhere and use the data collected to train a neural network, or you can input the network weights for the blue bird.



Start
📗 Genetic algorithm settings:
(1) Number of simulations per round:
(2) Number of children:
(3) Mutation probability:
📗 Neural network settings:
(1) Number of hidden units (h):
(2) First layer weights (2 xx h matrix):
(3) Second layer weights (h vector):
Output Best Green Reset Blue
📗 Training data:
Train Red