Articles
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An Incomplete List of Resources for Machine Learning
December 5, 2023
Here is a list of resources about probability, statistics, machine learning, etc. This list is not complete and is biasedMy by what I am interested in. I would highly suggest you to have a look at a few pages of these books and pick up whatever looks most appealing to you. Most of them are freely available online. If not, it should be possible to preview them on Google Books or Amazon.
Popular Science books
I highly recommend The Book of Why by Judea Pearl. It’s about understanding cause and effect. Pearl won the Turing award (the highest honour in Computer Science) for Bayesian networks, so he knows what he’s talking about. It’s written for a general audience, so there are few equations, and lots of historical context, stories and anecdotes.
If you are interested in causal inference after reading this, I wrote a blogpost on how to get started on reading about causality.
Textbooks
My favourite is Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi. I absolutely love the language and the way it is written. There is enough math to qualify it as a textbook, but it is also liberally peppered with quotes such as -
Every time someone thoughtlessly uses regression for causal inference, an angel not only loses its wings, but is cast out of Heaven and falls in extremest agony into the everlasting fire.
I think this is a good introductory book. It is entertaining to read and has many practical examples.
Bayesian Reasoning and Machine Learning by David Barber is a great, comprehensive book for Bayesian statistics.
A good introductory book to computational biology is Genomes, Networks and Evolution by Manolis Kellis.
Some other classic/trendy textbooks are -
Pattern recognition and machine learning by Christopher Bishop (also called The Bible, or The Bishop Book)
Reinforcement learning: An introduction by Sutton and Barto (also called Sutton and Barto, or The Reinforcement Learning book)
Mathematics for Machine Learning
Distill.pub - this is not a book, but a collection of interactive articles on deep learning
Lecture Series
Jonas Peters’ 4 part lecture on causality
David Silver’s 4 part lecture on reinforcement learning
3Blue1Brown YouTube channel - this is just an awesome math channel in general
Bonus
How much math can you understand in this video?
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Serve Static Files with Minimum Effort
March 2, 2020
Suppose you have a group of files that you want to share with a group of people.
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Using Causality to Understand AI Systems Better
February 21, 2019
This article originally appeared on LinkedIn pulse.
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Tools and Libraries for Causality
November 16, 2018
Here is a list of libraries, packages and tools for causal discovery and inference.
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The Road to Strong AI (According to Judea Pearl)
October 28, 2018
This post is a brief summary of Judea Pearl’s paper “Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution”
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Resources to get started on Causality
April 10, 2018
Here is a list of resources to get started on Causality, Causal Inference and Counterfactual Reasoning.
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Distributional Similarity vs Distributed Representation
September 15, 2017
If you are an NLP beginner (like me), then it is common to come across the terms distributional similarity and distributed representation in the context of word embeddings.
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How to use USB microphone with Android
September 7, 2017
I recently discovered the Sing! Smule Android app. Sing! is like a music social network of sorts - you can sing karaoke, either alone with others.
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Algorithms for Causal Discovery
February 7, 2017
This article considers the problem of estimating the structure of the causal DAG given some observational data.
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What is a 'Smart City' anyway?
October 14, 2016
In September 2015, CSTEP released a compendium of resources on Smart Cities.
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Estimating Parameters in a Bayesian Network
September 23, 2016
In this article, I will describe how to find the maximum likelihood estimates for parameters of a Bayesian Network. Intutively, a Bayesian Network provides a way to capture dependecies between different variables that we are interested in.
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Causal Library in Python: Part 1
September 23, 2016
In the ninth semester, we are required to work on a 12 credit project. My project is on building a python library for causal inference. I’m working on this with Chandan Yeshwanth.
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Personalised Hotel Recommendations
June 23, 2016
My friends - Amit Gupta and Madhumathi K - and I, attempted the Expedia Hotel Recommendations challenge as part of the project for the Machine Learning course. Details about the competetion can be found here.
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What's the Perfect Temperature? - The Politics of Indoor Air Temperature Control
October 24, 2015
“Can someone increase the temperature of the AC? It’s too cold!”
“What?! I’m practically sweating here! Don’t change the temperature.”