I got really interested in Computational Probability and Inference (6.008.1x) for the following reasons:

**I love probability**and have solved countless problems on probability ever since I learned math- …and yet I’ve
**never coded up probabilistic models!** - The assignments and project work for this course are to be implemented in
**Python**!

You don’t need to have prior experience in either probability or inference, but you should be comfortable with basic **Python programming** and **calculus**.

**WHAT YOU’LL LEARN**

– Basic discrete probability theory

– Graphical models as a data structure for representing probability distributions

– Algorithms for prediction and inference

– How to model real-world problems in terms of probabilistic inference

The course started on **September 12**, is **12-weeks long** and is structured in the following manner:

**Week 1 (9/12 – 9/16):** Introduction to probability and computation

A first look at basic discrete probability, how to interpret it, what probability spaces and random variables are, and how to code these up and do basic simulations and visualizations.

**Week 2 (9/19 – 9/23):** **Incorporating observations**

Incorporating observations using jointly distributed random variables and using events. Three classic probability puzzles are presented to help elucidate how to interpret probability: Simpson’s paradox, Monty Hall, boy or girl paradox.

**Week 3 (9/26 – 9/30):** **Introduction to inference, structure in distributions, and information measures**

The product rule and inference with Bayes’ theorem. Independence: A structure in distributions. Measures of randomness: entropy and information divergence. Mutual information.

**Week 4 (10/3 – 10/7):** **Expectations, and driving to infinity in modeling uncertainty**

Expected values of random variables. Classic puzzle: the two envelope problem. Probability spaces and random variables that take on a countably infinite number of values and inference with these random variables.

**Week 5 (10/10 – 10/14):** **Efficient representations of probability distributions on a computer**

Introduction to undirected graphical models as a data structure for representing probability distributions and the benefits/drawbacks of these graphical models. Incorporating observations with graphical models.

**Week 6 (10/17 – 10/21):** **Inference with graphical models, part I**

Computing marginal distributions with graphical models in undirected graphical models including hidden Markov models..

**Week 7 (10/24 – 10/28):** **Inference with graphical models, part II**

Computing most probable configurations with graphical models including hidden Markov models.

**Week 8 (10/31 – 11/4):** **Introduction to learning probability distributions**

Learning an underlying unknown probability distribution from observations using maximum likelihood. Three examples: estimating the bias of a coin, the German tank problem, and email spam detection.

**Week 9 (11/7 – 11/11):** **Parameter estimation in graphical models**

Given the graph structure of an undirected graphical model, we examine how to estimate all the tables associated with the graphical model.

**Week 10 (11/14 – 11/18):** **Model selection with information theory**

Learning both the graph structure and the tables of an undirected graphical model with the help of information theory. Mutual information of random variables.

**Week 11 (11/21 – 11/25):** **Final project**

Final project assigned

**Week 12 (11/28 – 12/2):** **Final project**

I’m SO taking this course. Hope this interests you as well!