This document summarizes a presentation about machine learning. It begins with a definition of machine learning as giving computers the ability to learn without being explicitly programmed. It then provides examples of tasks that machine learning can perform, such as spam filtering and stock market prediction. The document notes that machine learning works to some degree but not perfectly. It introduces a company called Nuroko that is building a machine learning toolkit with certain desirable properties such as being general purpose, powerful, scalable, real-time, and pragmatic. The document explains why the company chose Clojure as its programming language and provides an overview of some key machine learning concepts and abstractions like vectors, coders, tasks, modules, and algorithms. It concludes

1. Machine Learning Live
Clojure Conj 2012
Mike Anderson
@mikera mike@nuroko.com

2. Machine Learning – A definition
"Field of study that gives computers
the ability to learn without being
explicitly programmed.“
Arthur Samuel, 1959
Source: Good Old Wikipedia 2

3. Learning = Building functions from experience
Task Input Output
Simple mathematical x y = sin(x)
function
Spam filtering Text of an email Probability of email being
message spam (%)
Stockmarket Historical data on Expected price
prediction - Stock prices movements
- Economic indicators
Remembering Thought: Thought:
names “Who was that guy “Oh yes – that was Bob”
who liked windsurfing?
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4. The state of machine learning
“It works! sort of…. sometimes…. on a good day….”
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5. nuroko.com
We’re building a toolkit for machine learning that is:
• General purpose – works on any data
• Powerful – advanced algorithms to detect complex patterns
• Scalable – handle unlimited data at internet scale
• Realtime – suitable for online use in real applications
• Pragmatic – designed for solving real problems
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6. Why Clojure?
Productivity and fun!
Good parts of the JVM
REPL
Interactive experiments
Functional programming
DSLs with composable abstractions
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7. Some Key Abstractions
Vector 1 0 1 1 0
Efficiently represents information as a
vector of double values
Converts arbitrary data into vectors (and
Coder “Cat” 1 0 1 1 0
back again!)
𝑜𝑢𝑡𝑝𝑢𝑡 = 𝑓 𝑖𝑛𝑝𝑢𝑡 Represents a problem to solve – typically
Task
via provision of training examples
Represents a function
Module - (e.g. a Neural Network)
Algorithm Adjusts parameters in a module to learn a
function from experience / data
- (e.g. back-propagation)
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8. Neural Networks
Output layer
Direction of
Hidden layer
calculation
Weighted connections
Input layer
Each node’s value is
computed as a function
of the weighted sum of its
inputs:
𝑦𝑖 = 𝑓 𝑤 𝑖𝑗 . 𝑥 𝑗
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9. How to train a neural network
(BASIC version)
10 Initialise network with some random weights
20 Choose a random training example as input
30 Compute the output
40 Determine error (difference vs. expected output)
50 Adjust the weights very slightly to reduce the error
60 GOTO 20
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10. Live Demo – Part 1
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11. A harder problem….
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12. A trick – compression of data
784 outputs
decompressor
150 units (“bottleneck”)
compressor
784 inputs
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13. Putting it together
2
10 outputs (one for each digit)
150 units (compressed data)
compressor
784 inputs
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14. Live Demo – Part 2
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15. Questions?
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