The document provides an overview of machine learning, defining it as the ability of computers to learn from data without explicit programming. It outlines different types of machine learning, including supervised, unsupervised, reinforcement, semi-supervised, and active learning, along with examples and applications for each. It also highlights the importance of big data in the success of machine learning and provides resources for further learning.

1. Types of machine
learning
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2. What is machine learning?
Arthur Samuel (1959): ”Machine learning is about giving computers the ability to learn without
being explicitly programmed."[1]
Bottom-up approach
◦ Data-driven
◦ Use a learning algorithm that extracts patterns from data
◦ Opposite of classic-AI (handcrafted rules)
No direct control
◦ Choice of algorithm (but not much else)
◦ Parameters of the algorithm
◦ Not possible to include explicit rules or directly affect the outcome
Most successful field of AI
◦ Facilitated by the big data explosion
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3. Types of machine learning
Core fields and applications
◦ Supervised Learning
◦ Unsupervised Learning
◦ Reinforcement Learning
◦ Semi-supervised learning
◦ Active learning
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4. Supervised learning
Input data
Output predictions
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5. Supervised learning examples
Predictive analytics
Demand forecasting
Finance
Text classification
Object recognition
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6. Unsupervised learning
Input data
Output patterns
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7. Unsupervised learning examples
Dimensionality reduction
◦ PCA
◦ Factor analysis
Clustering
◦ K-means
◦ DBSCAN
◦ Affinity propagation
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8. Reinforcement learning
“Life simulation”
Agent knows:
◦ The state
◦ Result of past actions
Agent needs to
◦ Take action in order to maximise cumulative
reward
◦ Might have to forego reward now to
get a larger one in the future!
E.g. studying for college
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9. Reinforcement Learning: AlphaGO
October 2015
RL system based on deep learning beat a human grand master
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10. Semi-supervised learning
Many domains have a large amount of unlabeled examples
◦ Not useful as unlabeled
Semi-supervised learning concept
◦ Use classifier on labelled data
◦ Label some of the unlabeled data based on the classifier, when high confidence exists
◦ Iterate
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11. Active learning
Not all examples are created equal
Use an oracle (e.g. human or a DB) to ask about the truth of the most informative examples
In active learning there are many strategies to find the most informative examples
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12. Learn more
Tesseract Academy
◦ http://tesseract.academy
◦ https://www.youtube.com/watch?v=QEBDeIw60FA&index=14&list=PLVce3C5Hi9BBfabvhEzYQTQDYEg2v
tuxH
◦ https://www.youtube.com/watch?v=WxY2gvLsw7k&index=15&list=PLVce3C5Hi9BBfabvhEzYQTQDYEg2v
tuxH
◦ Data science, big data and blockchain for executives and managers.
The Data scientist
◦ Personal blog
◦ Covers data science, analytics, blockchain, tokenomics and many more subjects
◦ http://thedatascientist.com/

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