Monthly Archives: December 2017

Neural networks – explained

As mentioned here a few times, I teach an executive course called Analytics for strategic management, as well as a short program (three days) called Decisions from Data: Driving an Organization on Analytics. We have just finished the first version of both of these courses, and it has been a very enjoyable experience. The students (in both courses) have been interested and keen to learn, bringing relevant and interesting problems to the table, and we have managed do what it said on the tin (I think) – make them better consumers of analytics, capable of having a conversation with the analytics team, employing the right vocabulary and being able to ask more intelligent questions.

Of course, programs of this type does not allow you do dive deep into how things work, though we have been able to demonstrate MySQL, Python and DataRobot, and also give the students an understanding of how rapidly these things are evolving. We have talked about deep learning, for instance, but not how it works.

But that is easy to fix – almost everything about machine learning is available on Youtube and in other web channels, once you are into a little bit of the language. For instance, to understand how deep learning works, you can check out a series of videos from Grant Sanderson, who produces very good educational videos on the web site 3 blue one brown.

(There are follow-up videos: Chapter 2, Chapter 3, and Chapter 3 (formal calculus appendix). This Youtube channel has a lot of other math-related videos, too, including a great explanation of how Bitcoin works, which I’ll have to get into at some points, since I keep being asked why I don’t invest in Bitcoin all the time.)

Of course, you have to be rather interested to dive into this, and it certainly is not required read for an executive who only wants to be able to talk intelligently to the analytics team. But it is important (and a bit reassuring) to note the mechanisms employed: Breaking a very complex problem up into smaller problems, breaking those up into even smaller problems. solving the small problems by programming, then stepping back up. For those of you with high school math: It really isn’t that complicated. Just complicated in layers.

And it is good to know that all this advanced AI stuff really is rather basic math. Just applied in an increasingly complex way, really fast.

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Analytics projects

asm_topTogether with Chandler Johnson and Alessandra Luzzi, I currently teach a course called Analytics for Strategic Management. In this course (now in its second iteration), executive students work on real projects for real companies, applying various forms of machine learning (big data, analytics, whatever you want to call it) to business problems. We have just finished the second of five modules, and the projects are now defined.

Here is a (mostly anonymised) list:

  • The Agency for Public Management and eGovernment (Difi) wants to understand and predict which citizens are likely to reserve themselves against electronic communications from the government. The presumption is that these people may be mostly old, not on electronic media, or in other ways digitally unsophisticated – but that may not be true, so they want to find out.
  • An electric power distribution company wants to investigate power imbalances in the electric grid: In the electric grid, production has to match consumption at all times, or you will get (sometimes rather large) price fluctuations. Can they predict when imbalances (more consumption that production, for instance) will occur, so that they can adjust accordingly?
  • A company in the food and beverage industry want to offer recommendations to their (business) customers: When you order products from them, how can they suggest other products that may either sell well or differentiate the customer from the competition?
  • A petroleum producing company wants to predict unintended shutdowns and slowdowns in their production infrastructure. Such problems are costly and risky, but predictions are difficult because they are rather rare – and that creates difficulties with unbalanced data sets.
  • A major bank wants to look into the security profiles of their online customers and investigate whether some customers are less likely to be exposed to security risks (and therefore may be able to use less cumbersome security procedures than others).
  • An insurance company wants to investigate which of their new customers are likely to leave them (churn analysis) – and why. They want to find them early, while there is still time to do something to make them stay.
  • A ship management company wants to investigate the use of certain types of oil and optimise the delivery and use of it. (Though the oil is rather specialised, the ships are large and the expense significant.)
  • Norsk Tipping runs a service helping people who are in danger of becoming addicted to gaming, an important part of their societal responsibility which they take very seriously. They want to identify which of their customers are most likely to benefit from intervention. This is a rather tricky and interesting problem – you need to identify not only those who are likely to become addicted, but also make a judgement as to whether the intervention (of which there is limited capacity) is likely to help.
  • A major health club chain wants to identify customers who are not happy with their services, and they want to find them early, so they can make offers to activate them and make them stay.
  • A regional bank wants to identify customers who are about to leave them, particularly those who want to move their mortgage somewhere else. (This is also a problem of unbalanced data sets, since most customers stay.)
  • A major electronic goods retailer wants to do market basket analysis to be able to recommend and stock products that customers are likely to buy together with others.

All in all, a fairly typical set of examples of the use of machine learning and analytics in business – and I certainly like to work with practical examples with very clearly defined benefits. Now – a small matter of implementation!