The book Rich Dad, Poor Dad by Robert Kiyosaki is probably the worst book of the personal-finance genre I have ever read, containing numerous bad advice and generally encouraging hubris instead of prudence in financial decision-making (in the name of entrepreneurism). I did, however, pick up one useful lesson from it: In finance, you need to … More Quality Financial Journalism: A List
Everyone with a rudimentary understanding of text analytics knows about term-frequency-inverse-document-frequency (TF-IDF) vectors. What is less known but deserve to be more widely appreciated and applied is a little related trick called Explicit Semantic Analysis (ESA) introduced by E. Gabrilovish and S. Markovitch, for which they were awarded the 2014 IJCAI-JAIR Best Paper Prize. (https://www.jair.org/bestpaper.html) ESA … More From Words to Concepts: Explicit Semantic Analysis
As part of a broader data science project, I recently had the chance to undertake a digitisation project to augment the structured dataset we have for analysis. The project turned out to be quite instructive and I came away with a few lessons, lessons I hope to share in this blog piece. The actual digitisation problem is … More A Digitisation Project: Fun with Tesseract
Unifying logic and probability is an active and ongoing research topic of great interest to many. There are many proposals of probabilistic logics in the literature, each with a different motivation, either computational or philosophical, and a different system of syntax and semantics. This state of affairs is confusing and not satisfactory, especially in view … More Unifying Logic and Probability for Learning
When I was building the Data Science Centre of Excellence at Reliance Industries, I once interviewed a world-class researcher who has worked at multiple institutions, including Yahoo and Google, and he told me something I didn’t understand till then. I thought a company like Google has a weak business moat because it is vulnerable to … More The Competitive Moat of Google, Facebook and Other Data Owners
Much has been written about the merits of passive index investing using Exchange Traded Funds (ETFs). Even for active value investors, I believe there is sometimes a place for ETF investing for people wishing to move into a new market, possibly just outside their circle of competence, in a low-risk fashion. The way I think about … More ETF Investing using Shiller’s CAPEs
Here is a short (and somewhat unusual) course on statistical machine learning that I have delivered multiple times over the last few years. Introduction to Statistical Learning Theory Bayesian Probability Theory Sequence Prediction and Data Compression Bayesian Networks In designing this course, I have deliberately steered away from the usual practice of giving students a (long) … More A Short Course on Statistical Learning
A major deficiency in many university-level computer science programs is neglect for training in fundamental mathematical skills. This deficiency usually rears its head when a CS student first move into an area like Data Science and quickly realise s/he does not even have the ability to fully understand papers and books in the field, let alone contribute … More How to Prove It
Colleagues and friends often ask me for book recommendations on data science. Here are seven books that teach some of the most important mental models on the limits of predictability and model building, as well as prediction techniques that actually work in practice. An Introduction to Probability and Inductive Logic by Ian Hacking Against the Gods: … More A Reading List for Data Scientists
I have been studying and experimenting with online learning algorithms for support vector machines (SVMs) for a while now, primarily with the intention of understanding how they can be used to learn SVM models on large multi-terabyte datasets. The following technical report describes the NORMA and PEGASOS family of algorithms and give some observations and relevant … More Online Support Vector Machines
Mental Models 4 Life 