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 Projects
- Scilab-Julia Interface
- Scilab Video Player
- Scilab server
- Xcos on Web
- Remote Triggered Virtual Lab
- FOSSEE Toolbox Project
- Front end development for FOSSEE websites
- Common Drupal based theme for FOSSEE Websites
- Develop GUI for OpenModelica using Python
- SBHS Virtual lab server and website
- Image Processing for Healthcare Research
- To Create Dynamic Syllabus Matrix for Virtual Labs Community portal
- Mapping Analytics from Virtual lab to edX
- Continuous integration for Virtual Labs
- Establishing P2P communication on edX mobile client
- Revive pythonFlu package for OpenFOAM
- Website development for Spoken Tutorial project
- Smart Mouse
- IIT-B App
- Tum Tum Tracker
- Learn To Flap
- Panorama in Cam-Scanner
- LendIt- A sharing platform
- Interactive textbook for Probability & Statistics
- Game-playing AI agents
- Competitive Programming
- Functional Programming
- Poisson Solver with Image Editing
- Blind Source Separation
Boosting is a well known machine learning technique, we use simple weak classifiers in cascade fashion to form a strong classifier. It’s extremely effective, facebook uses some version of this algorithm for detecting faces (99.9% accurate). Implement basic adaboost on simulated data, then for digit recognition.
Again implementation in python using opencv and must follow blockwise execution and tutorial format.
- Read up on the Adaboost algorithm.
- Study mathematical theory.
- Start implementation of basic Adaboost algorithm in Python.
- Finish code for Adaboost in Python.
- Vectorise the code using numpy. Speed up of about 10x.
- Read up on digit recognition via Adaboost.
- Begin writing code for digit recognition.
- Write Ipython notebook for Adaboost.
- Acquire training and testing data(images and labels).
- Complete digit classification code.
- Test the digit classifier on data.
- Debug and optimise output and successfully implement the digit classifier.
- Add digit classification to the Ipython notebook.
The project was completed in a little less than three weeks. The repo for the project can be found here. The rendered iPython notebook can be found at the official iPython Wiki or can be directly accessed here.