Welcome to Bayesian Classification!

This course is the sequel to Bayesian Linear Regression, and it's a part of my series on Bayesian Machine Learning. While the previous course looked at regression (predicting a numerical output), this course looks at classification (predicting a categorical output).

This course takes the Bayes classifier (which, despite its name, is not Bayesian), and makes it Bayesian by placing priors on its parameters. In this course we will study the Bayesian Bayes classifier through the lens of Naive Bayes, so it would be a good idea to have a good handle on Naive Bayes before starting this course.

How does this course compare to Bayesian Linear Regression? Bayesian Linear Regression introduced a lot of the necessary math needed for Bayesian Machine Learning, and it built upon the A/B Testing course (mainly the concept of conjugate priors and how to compute the posterior distribution). In this course, we will go faster through the math we've already seen, so that we can focus on the new and interesting parts. Unlike the Bayesian Linear Regression course, the real learning opportunity in this course is in implementing each algorithm you learn about.

Why Bayesian Machine Learning? The main advantage of using Bayesian Machine Learning is that it doesn't require you to find a best guess for the optimal model parameters (a point estimate). Instead, Bayesian ML allows us to integrate over*all possible* values of the parameters (of which there are usually an infinite number).

Suggested Prerequisites:

This course is the sequel to Bayesian Linear Regression, and it's a part of my series on Bayesian Machine Learning. While the previous course looked at regression (predicting a numerical output), this course looks at classification (predicting a categorical output).

This course takes the Bayes classifier (which, despite its name, is not Bayesian), and makes it Bayesian by placing priors on its parameters. In this course we will study the Bayesian Bayes classifier through the lens of Naive Bayes, so it would be a good idea to have a good handle on Naive Bayes before starting this course.

How does this course compare to Bayesian Linear Regression? Bayesian Linear Regression introduced a lot of the necessary math needed for Bayesian Machine Learning, and it built upon the A/B Testing course (mainly the concept of conjugate priors and how to compute the posterior distribution). In this course, we will go faster through the math we've already seen, so that we can focus on the new and interesting parts. Unlike the Bayesian Linear Regression course, the real learning opportunity in this course is in implementing each algorithm you learn about.

Why Bayesian Machine Learning? The main advantage of using Bayesian Machine Learning is that it doesn't require you to find a best guess for the optimal model parameters (a point estimate). Instead, Bayesian ML allows us to integrate over

Suggested Prerequisites:

- Python coding: if/else, loops, lists, dicts, sets
- Numpy and Pandas coding: matrix and vector operations, loading a CSV file
- Basic math: calculus, linear algebra, probability
- Naive Bayes classifiers
- Bayesian Machine Learning: A/B Testing in Python (know about conjugate priors)
- Bayesian Linear Regression: know about the posterior predictive distribution

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

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I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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- Introduction and Outline (02:45) (FREE preview available)
- Where to get the code (01:42)

- Bayes Classifier Review (06:09)
- Making the Bayes Classifier More Bayesian (09:47)
- Fit (20:45)
- Predict (19:16)
- Limitations, Extensions, Naive Assumption (04:50)
- Softmax (09:37)
- Theory Summary (10:21)
- Suggestion Box (03:10)

- Gaussian - Fitting (18:15)
- Gaussian - Predicting (19:53)
- Gaussian Code (26:26)
- Unknown Mean and Variance (Optional) (19:40)

- Poisson Likelihood (11:03)
- Poisson Code pt 1 (20:15)
- Poisson Code pt 2 (09:21)
- Poisson Code pt 3 (04:50)

- PDF Notes