WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Running Projects
- L.A.M.A. AI using Reinforcement Learning
- Intrusion Detection system
- Competitive coding
- Why The Hype Around GANs
- 3D reconstruction using 2D images
- Computer Vision Workbench
- 3D Object Classification using Mesh Neural Network
- Lossless high entropy compression algorithm
- ML GYM
- Tools for Web Development
- Strategy Wars [Online]
- Food Recommendation through Machine Learning
- Conversational Chatbot
- Virtual Keyboard
- Super Shenron
- Gestures for 3D space
- Road Network 3D Rendering using OpenGL
- Face Recognition using Statistics
- Introduction to Kaggle and Machine Learning
- Krittika Website
- Rubik's cube solver
- Planet/Atmosphere Renderer using OpenGL
- Digital Depth Perception
- KontaKt App
- Tinkerers’ Laboratory Website
- Graphic Intensive MUSIC APP
- Pool It!
- Insti Buddy
- Intelligent agents
Rubik’s cube is one of the most fascinating 3-D combination puzzles we encounter. It is simple to understand the game, given a permutation we need to reduce the cube to a single goal state by rotating it. In this project, we will accomplish solving this cube with as minimum rotations as possible using the ideas of reinforcement learning.
There will be three modules that need to be completed.
- Given the details about the faces of the cube, solve the puzzle in the back-end.
- Given an image, extract the details about the faces of the cube using various image processing and possibly ML techniques
- Develop an user interface for the above solution.
Tentative Project Timeline
|Week Number||Tasks to be Completed|
|Week 1||Learn python and python-on-android, play with Rubik’s cubes to get a clear grasp about the problem. Try pycuber, a python package for dealing with Rubik’s cubes, which helps in creating graphical user interface.|
|Week 2||Go through the available literature on the problem. Understand basic algorithms and concepts of Reinforcement learning pertaining to the problem.|
|Week 3||Try out various Q-learning, Deep Q-learning examples and MCTS.|
|Week 4||Implement Deep Q-learning and MCTS for our problem.|
|Week 5||Measure performance against benchmark and hyper-parameter tuning.|
|Week 6||Buffer week to finish up with documentation|
|Week 7||Given an image taken from a good viewpoint, extract the details.|
|Week 8||Create a graphical user interface to upload the images of the cubes and return with an animation of the solution.|
|Week 9||Buffer week to finish up with presentation and documentation|