Welcome to Statistics & Machine Learning
This course is about understanding how one can make machine infer in a broad context given a set of previously explored data. In fact, the situation arises in reality such as email editors automatically completing sentences, cell phones being able to identify faces, the future car predicting potential accidents and acting accordingly, and more. The essential questions of machine learning include: what is learning, what is inference, and how shall we justify on the basis of statistics? Implementation using Python will be instructed with the sci-kit learn library. Projects related to deep learning, natural language processing, and non-parametric Bayesian learning are encouraged.
Download the Jupyter Notebooks
Clone or download this Github repository, so you have access to all the Jupyter Notebooks (.ipynb extension) in the tutorial. Note the green button on the right side of the screen that says Clone or download. If you know how to use Github, go ahead and clone the repo. If you don't know how to use Github, you can also just download the zip file and unzip it on your laptop.