Machine Learning for Designers

by Patrick Hebron

Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O’Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs.
Using tangible, real-world examples, author Patrick Hebron explains how machine-learning applications can affect the way you design websites, mobile applications, and other software. You’ll learn how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction.
This report explains how to:
- Leverage machine-generated user insights to provide a more personalized customer or user experience
- Spot opportunities for the integration of machine-learning capabilities into existing designs and platforms
- Choose the right machine-learning platforms or services
- Design for the probabilistic and often imprecise nature of machine-generated data
- Stay up to date with advancements in the field and spot emerging opportunities for machine learning-aided design

Genres: Design

79 pages, ebook

Published June 1, 2016

Reviews

[Kars · Rating 4 out of 5]

Short but coherent and complete. Delivers on its promise. I particularly liked the final section on how to make machine learning part of the design process.

[Pratyush Pandab · Rating 4 out of 5]

The book is meant for aspiring designers who would like to explore the realms of Machine Learning and its possible applications. The author regards designers as design engineers who collaborate with programmers and have a basic understanding of computer science.

If I were to use one sentence to summarize this book for Designers, it would be “Start to spot opportunities for the integration of machine-learning capabilities into existing design platforms.”

The author emphasizes the collaboration between Designers and Programmers multiple times throughout the course of this book. This is imperative so as we designers can take the full advantage of Machine Learning systems’ vast technical capabilities.

The author explains a lot of terminologies related to Machine Learning. They include deductive versus inductive reasoning, deep learning algorithms, artificial neural networks, and many more. Now, they are not necessary for designers to understand in details, but by knowing them we sure will have some common ground when we talk with our programmer friends.

The author also provides examples wherever necessary. Object and pattern recognition, Image segmentation and tagging, face recognition, depth estimation, etc come under visual machine learning. Nowadays, we also have voice recognition devices such as Amazon’s Echo, Alibaba’s T mall Genie, Google Home, Siri, etc. These come under aural machine learning.

Lastly, in the author's own words, “Discovering the unique possibilities of a medium requires experimentation, a fresh pair of eyes, and a willingness to think outside of the existing paradigms. It is here that designers will prove essential to the future of machine learning.” ✌🏼

[Zane Riley · Rating 3 out of 5]

This was actually a useful overview for the different types of machine learning, but I think what I was looking for was "How to Use Machine Learning: For those who know just enough JavaScript to get by."

Quick read nonetheless, I'll likely use it as reference material in the future.

[Lucas Coelho · Rating 3 out of 5]

Short and sweet. Full of good references to dive deeper into Machine Learning.

That being said, if you are into machine learning and been following for a while, you can skip this book straight to medium articles and reddit or quora posts. ☺️

[Jay Yeo · Rating 5 out of 5]

Very good and clear introduction to machine learning. References many concepts from CS2401 Information Analytics and HY9311 Minds and Machines — glad I took those modules.

[Alex]

This free ebook is a quick read and a good intro to beginning to think about design machine learning experiences for people. It gives you enough of an overview and vocabulary for the field to know how to frame opportunities for ML within a design. It's important to recognize that this resource should be used to start thinking about ML as a designer but not a comprehensive resource.

Given the state of ML within digital products I wish it spoke more of the ethical implications of ML and how ML can be used poorly. This is not to say that the book does not speak to this point, but that I wished it spoke more strongly about it.

Something I would like to dive in more to is using machijne learning not as a solution but as a tool to better understand problems/situations by identifying patterns and relatuonships in data we might not have picked up on our own.

[Nadya Tsech · Rating 4 out of 5]

The book has two logical parts:
1. ML fundamentals (you can skip it. It doesn't have many examples or design-related content).
2. Design principles and emerging best practices.

Even though it focuses more on conversational ML and chats, principles from the book can be applied anywhere. I appreciate the chapter about prototyping.

[Radu · Rating 5 out of 5]

Short but the interesting book about machine learning. Not too technical (a plus from my perspective) you don’t have to be a designer to read it, as long as you want to better understand how ML could (and would) change the user experience.

[Chris Rivard · Rating 4 out of 5]

The fundamentals.

[Qing Wang · Rating 4 out of 5]

A well-written book, insightful and accessible.