Enterprise Artificial Intelligence and Machine Learning for Managers

I decided to read this short book published by C3.ai that is focused for managers. The book focuses on concepts and gives you the basic nomenclature to understand how these type of initiatives are implemented. Later, when you review the product list of C3 company, you realize where they are classified in terms of the standard AI classification.

Below, some notes of the concepts I would like to review in future.

The Author is Nikhil Krishnan, PhD.

Machine Learning categories

Common categories of Machine Learning algorithms

Main types of supervised learning

Supervised learning algorithms learn by tuning a set of model parameters that operate on the model’s inputs, and that best fit the set of outputs.

Examples of classification and regression techniques

Unsupervised learning

Unsupervised learning techniques operate without known outputs or observations – that is, these techniques are not trying to predict any specific outcomes. Instead, unsupervised techniques attempt to uncover patterns within data sets.

Unsupervised techniques include clustering algorithms that group data in meaningful ways.

Clustering algorithms

Unsupervised machine learning models do not require labels to train on past data. Instead, they automatically
detect patterns in data to generate predictions.

Dimensionality reduction

Dimensionality reduction is a powerful approach to construct a low-dimensional representation of high-dimensional input data. The purpose of dimensionality reduction is to reduce noise so that a model can identify strong signals among complex inputs – i.e., to identify useful information.

High dimensionality poses two challenges. First, it is hard for a person to conceptualize high-dimensional space, meaning that interpreting a model is non-intuitive. Second, algorithms have a hard time learning patterns when there are many sources of input data relative to the amount of available training data.

Example of an unsupervised machine learning model for anomaly detection.

Reinforcement learning

Reinforcement learning (RL) is a category of machine learning that uses a trial-and-error approach. RL is a more goal-directed learning approach than either supervised or unsupervised machine learning.

Deep Learning

Deep learning is a subset of machine learning that involves the application of complex, multi-layered artificial neural networks to solve problems.

Deep learning takes advantage of yet another step change in compute capabilities. Deep learning models are typically compute-intensive to train and much harder to interpret than conventional approaches.

A deep learning neural network is a collection of many nodes. The nodes are organized into layers, and the outputs from neurons in one layer become the inputs for the nodes in the next layer.

Single nodes are combined to form input, output, and hidden layers of a deep learning neural network.

Maps and civilization

I have picked this book to learn about maps and improve my knowledge on cartography, and see if it helps on the deeper understanding of Wardley Maps.

The amount and quality of data on the book is great and the author is so concise and direct, so you do not lose time reading extra pages that do not provide value.

If you want to learn about cartography basis, this book is for you.

Reaching Cloud Velocity: A Leader’s Guide to Success in the AWS Cloud

I bought this book because I knew there were some mentions to Wardley Maps, and I was curious about how they were used by AWS people.

When I received the book I was a little bit disappointed, as the book just contain a chapter where they introduce Wardley Maps as part of the business transformations they are used to perform in different companies they work with.

But here is the point, the whole book is an explanation of how to transform your company into a cloud based company, focusing on all aspects.

This left me a little bit bitter taste too, as Transformation and Transition Manager, I have done this type of work and all was very familiar to me, and my expectation was to find something completely different that let me learn.

The book is a good guide of all the aspects that you should have into account when doing a transformation such it. And it contains specific target models about how the target operating model can be and should be managed.

As in all transformations, this is a business topic not a technology topic, it was always the case.


WAKE THE F*CK UP, my dear: Stop. Rethink. Second chance.

I read this book as I know the author and I was sure it was going to be as funny as she is.

And the read was valuable and funny, so what else?

some notes for my poor memory:

  • Stop being a zombie
  • update your dogmas



Last but not least, I loved this one:

Watch your thoughts, they become words;
watch your words, they become actions;
watch your actions, they become habits;
watch your habits, they become character;
watch your character, for it becomes your destiny.

The Fifth risk

I bought this book in 2019 but I was not able to read it till now, with the quarantine I remembered I had this one on a shelf.

Very interesting to understand how the different governmental organizations work and how the transitions were completed (or not in this case).

I love the personal stories that Michael Lewis give us in this book.

The New Trading For a Living

This book written by Alexander Elder is a fantastic way to start learning trading.

I learn a lot from Systematic Trading about how to be systematic and how to manage your portfolio. But with this book I learn the different types of trading, how to focus on the behavior, how to build your own habits on specific papers, the importance of include the checklist on a daily basis, etc…

Don’t do it!!

Alexander is about 40 pages of the book persuading you to do not try it, he explains the different reasons trading is a risky and complex activity to be performed consistently along the time.

Risk management

  • rule of 2% for the maximum operations exposed by month.
  • Use risk exposure to handle your trades. The concept of “buying risk exposure” when you want to open more trades and you cannot do it.
  • rule of 6% for the maximum exposure of risk of your portfolio.
  • Understand how to adapt the use of slots and margins with respect the obtained results.

Classification of activities you have to do, how to engage general topics on your daily habits, how to socialize the trading with other people and learn from the previous data.

I pretend to be a Swing Trader

I learned reading the book that what I do is Swing Trading. My current scenarios are trades that can take hours, days or weeks, so now I know I want to become a swing trader.

This has enabled me to focus on how to organize the portfolio, how to divide the efforts on analysis, focus on specific tactics and measure the real results I obtain.

I have removed a lot of noise and wrong behaviors that did not help me on my duties.

Trading en la zona, de Mark Douglas

Este libro es muy interesante si lo que buscas es entender los principales aspectos psicológicos que el trading tiene sobre la persona. No solo describe los sesgos y comportamientos del trader ante las pérdidas y las ganancias, sino que además añade algunos mecanismos de como afrontar y planificar ciertas situaciones que se te pueden dar.

Es muy recomendable su lectura ya sea para un aprendiz de trader como para alguien que quiere dedicarse a la inversión en acciones a largo plazo.

El pequeño libro que aun vence al mercado de Joel Greenblatt

He leído este libro por recomendación de varios blogs que lo recomendaban como un libro altamente útil.

Pues bien, a mi no me ha parecido nada del otro mundo. Joel repite hasta la saciedad que su fómula mágica funciona en múltiples ocasiones, de hecho creo que si quitas toda esa paja, entonces el libro se queda en un 60%.

La fórmula mágica es bien sencilla. Ir a cualquier web de finanzas donde puedes filrar empresas por distintos parámetros, por ejemplo: yahoo finance. Después filtrar las empresas con alto ROCE, un EBIT/EV razonable, poca deuda, quitar sectores que no te gusten (Joel quita bancos, aerolineas…) y algún filtro más.

Con la lista que empresas, quédate con unas 10 – 20, y ve invirtiendo poco a poco. Después vas rotándolas con nuevas empresas al año y algunos días para evitar pagar masivamente impuestos por ganancias.

Si hubiera leído este libro hace 10 años me hubiera venido muy bien, pero ahora la verdad es que me ha sobrado.

Allegro ma non troppo, Marco Cipolla

I read this book because it was recommended by different people so I decided to buy it. It’s a nice book and it’s very light but apart of the review of the impact of the black pepper in Europe, I did not learn so much else.

I had more expectations based on the reviews of the book, and it did not meet my expectations.

Systematic trading by Robert Carver

I was looking for a trading book that goes beyond to the typical basic concepts and that send messages as “this is the book that will make you millionaire”.

Well, this is the first book I found with this characteristics. Very complete one, and complex enough that is going to force me to review many concepts again in detail.

Any other suggestion for next book?