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)
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)