This document provides an overview of machine learning and how it gains more importance in the age of big data. It discusses machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning. It also contrasts traditional data science with machine learning approaches and explains how machine learning works better with large, big data. A key point made is that more data is better for machine learning algorithms to learn from than having the best algorithm alone.

1. Daniel Sarbe
Development Manager, BigData and Machine Translation
SDL Research
Twitter: @danielsarbe
Machine Learning in the age
of Big Data

2. Agenda
• Machine Learning overview
• Big Data overview
• Why Machine Learning gain more importance in BigData age?
• Demo
• Q&A

3. What is Machine Learning?
• Machine learning
• field of study that gives computers the ability to learn without being
explicitly programmed
• Arthur Samuel in 1959 - chess program
• make intelligent decisions and predictions
based on your data
• ML algorithms
• essentially, just probabilities and statistics
• are math, they are not magic
• can be done on paper but it takes too much
• machines can do it really well

4. Machine Learning Styles
1. Supervised Learning - Learning from labeled data
• Regression
• Predicting house price
• Weight based on height
• Classification
• Spam filtering
• OCR
2. Unsupervised learning - Learning from unlabeled data
• Clustering
• Recommendation systems (e.g. Amazon/Netflix)
• Grouping related web news (e.g. Google News)

5. Traditional vs ML

6. Typical ML process

7. “It's not who has the best algorithm that wins. It's who has the
most data.”
Andrew Ng

8. What is BigData?

9. What is BigData?

10. ML and BigData
• ML works better on Big data
• We don’t need lots of things to learn, if we have a huge data
• What roles plays human in this?

11. Batch vs Online Learning Algorithms
• Batch
• has access to the entire training data set
• Online
• algorithm receives feedback about each prediction
• feedback is used to improve the accuracy on subsequent predictions.
• has to make predictions continuously (ad-hoc learning)

12. Language Learning Demo
• What is the problem that we are trying to solve?