This is a short (1 week) course aimed towards a formal introduction to the basics of probability theory and machine learning. The goal of the first half of the course is to make us comfortable and rigorously reason about probability arising in the context of computer science and machine learning. In the second half of the course, we will start with basics of Machine Learning and build our way up to Neural Networks.
Day 1: PyTorch Setup and Introduction to Probability
Please find the code we used for MLE for coins here.
As a guideline for the homework, we are also uploading the code for estimating bias of a 6-sided dice here.
Toy Example | Stock Market
The goal of this homework is to predict the movement of a dummy stock-market. You will be given data points: . The data point corresponds to the movement of the stock-market on day. Assume that there are working hours, and corresponds to whether the market went up or down during hour on day. That is, if market went up, and if it went down.
Assume that market movements on different days are i.i.d. You may not assume anything about dependence of the market movement during a single day. For example, it may happen that if market went up in first hour, it will surely go down in the last hour irrespective of what happened in the middle three hours.
You can find the data for 10,000 days of market movement here. Your goal is to predict market movement on the last hour for the 10 days of data present here.