Advanced Topics in CS, Fall 2024

Please use your reply to this blog post to detail the following:

1. Please give a full description of your final project. Based on your prior work this semester, what made you pick this as your project?
2. What was your desired learning outcome of your choice of final project?
3. What has been the most useful aspect of this class? Learning more about Python, GitHub, PyCharm, AI, ML, or …? You decide and please explain why.
4. Do you feel your work this semester, as summarized by your choice of final project, has helped you better understand some of the foundations of ML and AI?
5. Do you see yourself pursuing data/analytical sciences coursework once you get to college? Do you anticipate being ahead of some of your classmates thanks to the things you studied this semester?
6. Include your Github repo URL so your classmates can look at your code.
7. Of all of the projects you or your classmates created this semester, which one was your favorite, and why?

Take the time to look through the project posts of your classmates. If you saw any project or project descriptions that pique your interest, please reply or respond to their post with feedback. Constructive criticism is allowed, but please keep your comments civil.

THANKS FOR TAKING THE COURSE!!!

Please use your reply to this blog post to detail the following:

1. Please give a full description of the nature of your A* project. How did you implement the assignment? Did you use Pygame or some other UI environment? Why did you choose the environment you selected?
2. What was the steepest part of the learning curve for this project? Please elaborate and explain your answer.
3. What was the best online tutorial you found to help you understand and implement the A* algorithm? If you used more than one resource, list all the URLs.
4. How could this assignment be improved for future AdvCS classes?
5. Include your Github repo URL so your classmates can look at your code.

Take the time to look through the project posts of your classmates. If you saw any project or project descriptions that pique your interest, please reply or respond to their post with feedback. Constructive criticism is allowed, but please keep your comments civil.

Please use your reply to this blog post to detail the following:

1. Please give a full description of the nature of your first AI/ML game project.
2. What was the steepest part of the learning curve for this project? Was it learning how to implement the AI or how to use the Pygame/Pyglet library? Please elaborate and explain your answer.
3. What went “right” with your project? As in, what worked seamlessly? What went “wrong” with your project? As in, what were your biggest hurdles or where did you have the most trouble debugging or getting your project to run?
4. Describe the AI/ML algorithm your game implements. Did you work through a tutorial you found online? Did you start from scratch because you were motivated by a particular game or algorithm and you wanted to implement it using Pygame/Pyglet?
5. If you had to teach this class next year, what project would you recommend to students in the Advanced Topics class to give them a broad and comprehensive overview of some fundamental AI algorithms to implement in a game?
6. Include your Github repo URL so your classmates can look at your code.

Take the time to look through the project posts of your classmates. If you saw any project or project descriptions that pique your interest, please reply or respond to their post with feedback. Constructive criticism is allowed, but please keep your comments civil.

Please use your reply to this blog post to detail the following:

1. Please start with a full description of the nature of your first ML project.
2. What was your primary motivation to explore the project you completed?
3. Share some insights into what predictions you were able to make on the dataset you used for your project.
4. What new skills did you pick up from this project? For example, had you used Jupyter Notebooks before? Did you encounter any weird bugs, twists, or turns in your dataset that caused issues? How did you resolve those issues?
5. What types of conclusions can you derive from the images/graphs you’ve created with your project? If you didn’t create charts or graphs and instead explored things like Markov Chains, how much work do you think you need to do to further refine your project to make its output more realistic?
6. Did you create any formulas to examine or rate the data you parsed?

Take the time to look through the project posts of your classmates. You don’t have to comment on the posts of your classmates, but if you want to give them praise or just comment on their project if you found it cool, please do.