⭐ 🌍 Welcome to the Machine Learning and Python repo for beginners ⭐ 🌍

In this repo I will provide all the weekly resources and lecture notes for the course that will take place in the Warrington Room at Harris Manchester College, Thursday 15:00-16;30 for 6 weeks. Excluding the week Dec 8th-15th.

Here's a brief outline of what to expect:

Week 1

In week 1 we will cover how to set-up Python for beginners, in particular Python 3+ for Windows (DOS) and Mac/Linux (UNIX) machines.

I will show you how to set-up a Python environment and how how to install different libraries.

• [:seedling:] How to download Python 3+ and Python 2.7 on your machine. (15 minutes)
• [:seedling:] How to set-up your Python environment. (20 minutes)
• [:seedling:] Switching between different Python versions and setting up new environments. (15 minutes)
• [:seedling:] How to run simple Python programs. (15 minutes)
• [:seedling:] How to get the required libraries for extending the base Python via pip and conda. (5 minutes)
• [:seedling:] How to get the required libraries via an open-source repo. (15 minutes)

Bonus material, if time allows

• [:seedling:] Basic Matrix and vector operations with NumPy (or PyTorch) (15 minutes).

Week 2

In week 2 we will focus on linear and non-linear regression.

• [:seedling:] Informal introduction to linear. (15 minutes)
• [:seedling:] Mathematical introduction to linear regression. (30 minutes)
• [:seedling:] Code up linear regression. (45 minutes)

Bonus material, if time allows

• [:seedling:] Introduction to non-linear regression. (35 minutes)

Week 3

In week 3 we will focus on logistic regression.

• [:seedling:] Informal introduction to linear. (15 minutes)
• [:seedling:] Mathematical introduction to linear regression. (30 minutes)
• [:seedling:] Code up linear regression. (45 minutes)

Week 4

In week 4 we will focus on Gaussian Processes.

• [:seedling:] Introduction to Gaussian Processes (GPs) and why they are important. (45 minutes)
• [:seedling:] Code up, manually, simple GP with squared exponential kernel. (45 minutes)

Course will break on here Dec 8th due to work commitments, but will resume from Dec 18th.

Week 5

In week 5 we will focus on Bayesian statistics - Bayesian stats allows you to quantify uncertainty in your models.

• [:seedling:] Introduction to Bayesian statistics and why it is important. We will also discuss Simpsons paradox. (35 minutes)
• [:seedling:] Bayesian linear regression. (15 minutes)
• [:seedling:] Code up Bayesian regression example. (35 minutes)

Bonus material, if time allows

• [:seedling:] Informal introduction to inference algorithms (15 minutes)

Week 6

In week 5 we will focus on Probabilistic Programming and Machine Learning frameworks.

• [:seedling:] Introduction to Prob prog and a very short introduction to inference algorithms. (25 minutes)
• [:seedling:] Code up so Bayesian regression models in pyro/ prob prog. (45 minutes)
• [:seedling:] GPs in Pyro. (30 minutes)

Week 6 + TBD