@ -24,6 +24,7 @@ As a prerequisite, please take the "Responsible AI Principles" Learn Path and wa
Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
[](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
> 🎥 Click the image above for a video: Microsoft's Approach to Responsible AI
@ -30,7 +30,8 @@ Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification)
✅ Take a moment to imagine a dataset about cuisines. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to use fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?
> The whole premise of the show 'Chopped' is the 'mystery basket' where chefs have to make some dish out of a random choice of ingredients. Surely a ML model would have helped!
> 🎥 Click the image above for a video.The whole premise of the show 'Chopped' is the 'mystery basket' where chefs have to make some dish out of a random choice of ingredients. Surely a ML model would have helped!
@ -54,6 +54,7 @@ In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](ht
This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response.
[](https://youtu.be/RMK9AphfLco "Chatting with Eliza")
> 🎥 Click the image above for a video about original ELIZA program
> Note: You can read the original description of [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) published in 1966 if you have an ACM account. Alternately, read about Eliza on [wikipedia](https://wikipedia.org/wiki/ELIZA)
In this lesson and the following one, you will learn a bit about time series forecasting, an interesting and valuable part of a ML scientist's repertoire that is a bit lesser known than other topics. Time series forecasting is a sort of crystal ball: based on past performance of a variable such as price, you can predict its future potential value.
[](https://youtu.be/wGUV_XqchbE "Introduction to time series forecasting")
[](https://youtu.be/cBojo1hsHiI "Introduction to time series forecasting")
> 🎥 Click the image above for a video about time series forecasting
It's a useful and interesting field with real value to business, given its direct application to problems of pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights in the prediction of future performance, time series forecasting remains a field greatly informed by classic ML techniques.
Jen Looper
3 years ago