In this second session, we will continue to explore machine learning with data driven examples.

Binary classification is the canonical machine learning task and its study has a rich history. Much of the main principles of machine learning have been discovered in this context. These principles continue to guide the broader development of machine learning, and they motivate methods such as cross-validation, kernel support vector machines, logistic regression, and neural networks. We will highlight the resultant methods and will accompany this with data driven examples.

Prerequisites: Bring a laptop pre-loaded with Jupyter, Python, NumPy and Scikit-Learn (they can be installed through [Anaconda][]).