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

Siddharth Singh
Manan Soni

Contents

The summer course is broadly divided into 3 modules:

Module 1: Python and Numpy

• 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.

Module 2: Genetic Algorithms and Neural Networks

• 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.

Module 3: Racing cars

• 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

License

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