Welcome to IEEE Projects!

Increase your technical skills with one of our four projects in several different technical areas ranging from full stack development, to artificial intelligence, and to electrical engineering. This semester, we are offering artificial intelligence and data science projects!

Projects will take ~5 hours/wk of committment. If you're interested in applying computer science skills into practical applications, we highly encourage you to apply using the button on the right!

Please refer below for a detailed list of project descriptions.

Fall 2021 Projects

2048 AI

DIY ML Prediction

2048 AI (Artificial Intelligence)
Members participating in this project will be able to learn the foundational concepts of AI, such as zero-sum games and minimax, and use their mathematical creativity to create an AI (from scratch) to play the game 2048.

This project is done through Python.

Rough schedule:
Week 1~2: Learning about the foundational concepts
Week 3~5: Making the minimax algorithm work, achieve automatic gameplay
Week 6~7: Refine heuristics to increase game winrate
Week 8~9: Apply runtime optimizations to further increase game winrate
Week 10: Battle against each other’s AI
Prerequsites: Previously or concurrent CS61A knowledge will be required for Python and trees
DIY ML Predictions (Data Science)
Members participating in this project will learn to scrape and collect datasets of their choice, and go onto find meaningful correlations between real-world variables using PCA. Towards the latter half of the project, members will be taught introductory Machine Learning models such that they can develop their own prediction model.

This project is done through Python.

Rough schedule:
Week 1~2: Selecting field of study and collecting datasets
Week 3~4: Introductory analysis using simple data science methods
Week 5~7: Using Principle Component Analysis to derive correlations between variables
Week 8~9: Creating a ML prediction model
Week 10: Summarizing data and results
Prerequsites: Data 8 or CS 61A required for Python knowledge, EECS 16A or Math 54 required for linear algebra knowledge, Data 100 recommended but not required for more advanced analysis