ML-SIG-2019

This is the official repository for BITS-ACM ML summer SIG - 2019

UPDATE for an interactive and succint introduction to Machine Learning, check out our introductory lecture.

The team

The summer course is broadly divided into 3 modules:

This module teaches you the basics of the Python programming language along with a few other useful tools
After completion you should be able to:
- Know how to write simple programs in Python
- Know the basics of git and Jupyter notebooks
- Learn how to write optimized code using the numpy library.
To complete: finish task-01-numpy/numpy_tasks.ipynb
For more details: refer to the task readme, the task pdf.

In this module you will learn about Genetic Algorithms, Neural Networks and everything in between!
After completion you should be able to:
- Implement your own Genetic Algorithms
- Understand the basic working of Neural networks
- Implement a simple feedforward network
To complete: finish task-02-GA-NN/GA_task.ipynb and neural_net.py
For more details: refer to the task readme, the task instructions and the provided resources.

Enough theory. In this module, we will build intelligent cars and race them against each others!
After completion you should be able to:
- Learn how to use Genetic algorithms and Neural networks in practice
- Get a taste of how models are trained and tested
- Design cars which can efficiently maneuver any given track
To complete: Submit your own car (preferably as a Jupyter notebook)
For more details: refer to the task readme and the 2 given examples: simple example and ML-GA example

PS: If we get enough submissions, we will organize a community race and display the results on #ML-SIG

This code has no license whatsoever.
Feel free to use and/or modify the code provided to your heart's content!