AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

402: Optimisation and Gradient Descent

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Optimisation

When discussing regression we found that these have closed solutions. I.e. solutions that can be solved directly. For many other algorithms there is no closed solution available.

In these cases we need to use an optimisation algorithm. The goals of these algorithms is to iteratively step towards the correct result.

Gradient descent

Given a cost function, the gradient decent algorithm calculates the gradient of the last step and move in the direction of that gradient.

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401: Linear Regression

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Regression and Linear Classifiers

Traditional linear regression (a.k.a. Ordinary Least Squares) is the simplest and classic form of regression.

Given a linear model in the form of:

\begin{align} f(\mathbf{x}) & = w_0 + w_1x_1 + w_2x_2 + \dots \\ & = \mathbf{w} ^T \cdot \mathbf{x} \end{align}

Linear regression finds the parameters \(\mathbf{w}\) that minimises the mean squared error (MSE)…

The MSE is the sum of the squared values between the predicted value and the actual value.

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302: How to Engineer Features

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Engineering features

You want to do this because:

  • Reduces the number of features without losing information
  • Better features than the original
  • Make data more suitable for training

???

Another part of the data wrangling challenge is to create better features from current ones.

Distribution/Model specific rescaling

Most models expect normally distributed data. If you can, transform the data to be normal.

  • Infer the distribution from the histogram (and confirm by fitting distributions)

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301: Data Engineering

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Your job depends on your data

The goal of this section is to:

  • Talk about what data is and the context provided by your domain
  • Discover how to massage data to produce the best results
  • Find out how and where we can discover new data

???

If you have inadequate data you will not be able to succeed in any data science task.

More generally, I want you to focus on your data. It is necessary to understand your data before building a model.

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203: Examples and Decision Trees

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Example: Segmentation via Information Gain

There’s a fairly famous dataset called the “mushroom dataset”.

It describes whether mushrooms are edible or not, depending on an array of features.

The nice thing about this dataset is that the features are all catagorical.

So we can go through and segment the data for each value in a feature.

This is some example data:

poisonouscap-shapecap-surfacecap-colorbruises?
pxsnt
exsyt
ebswt
pxywt
exsgf

etc.

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202: Segmentation For Classification

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Segmentation

So let’s walk through a very visual, intuitive example to help describe what all data science algorithms are trying to do.

This will seem quite complicated if you’ve never done anything like this before. That’s ok!

I want to do this to show you that all algorithms that you’ve every heard of have some very basic assumption of what they are trying to do.

At the end of this, we will have completely derived one very important type of classifier.

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201: Basics and Terminology

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The ultimate goal

First lets discuss what the goal is. What is the goal?

  • The goal is to make a decision or a prediction

Based upon what?

  • Information

How can we improve the quality of the decision or prediction?

  • The quality of the solution is defined by the certainty represented by the information.

Think about this for a moment. It’s a key insight. Think about your projects. Your research. The decisions you make. They are all based upon some information. And you can make better decisions when you have more good quality information.

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102: How to do a Data Science Project

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Problems in Data Science

  • Understanding the problem

  • “the five-whys”

  • Different questions dramatically effect the tools and techniques used to solve the problem.

Data Science as a Process

  • More Science than Engineering
Research Problem Model
  • High risk
  • High reward
  • Difficult
  • Unpredictable
CRISP-DM Process

By Kenneth Jensen CC BY-SA 3.0, via Wikimedia Commons

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101: Why Data Science?

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What is Data Science?

  • Software Engineering, Maths, Automation, Data

  • A.k.a: Machine Learning, AI, Big Data, etc.

  • It’s current rise in popularity is due to more data and more computing power.

For more information: https://winderresearch.com/what-is-data-science/

Examples

US Supermarket Giants

  • Target: Optimising Marketing using customer spending data.

  • Walmart: Predicting demand ahead of a natural disaster.

Discovery

  • Most projects are “Discovery Projects”.

  • Primary Business goals: Increase Revenue, save costs, save time.

  • Budgets can come from other parts of the business.

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Testing Model Robustness with Jitter

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Testing Model Robustness with Jitter

Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos.

To test whether your models are robust to changes, one simple test is to add some noise to the test data. When we alter the magnitude of the noise, we can infer how well the model will perform with new data and different sources of noise.

In this example we’re going to add some random, normally-distributed noise, but it doesn’t have to be normally distributed! Maybe you could add some bias, or add some other type of trend!

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