machine learning - eden au · supervised learning is the machine learning task of learning a...
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Machine LearningEden Au, James Fulton
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3http://joelgrus.com/2013/06/09/post-prism-data-science-venn-diagram/
4https://uk.mathworks.com/discovery/machine-learning.html
Supervised Learning
“ Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
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The Machine● Linear regression ● K-nearest neighbours ● Support Vector Machines ● Random Forest● Naïve bayes
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9Wikipedia
Linear Regression - Ordinary Least Squares1. Linearity (solved by feature engineering e.g. polynomial regression)2. Error-free inputs (solved by generalized least squares)3. Common variance (solved by weighted least squares)
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Wikipedia
K-nearest neighbours1. Inherently non-linear
(non-parametric)2. Simple3. Parameter selection4. High memory requirement -
slow prediction stage
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Main Challenges - Feature Engineering
Shallow learning requires feature engineering
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ArtificialNeural Network
Neural NetworksEach layer consists of
1. Linear regression2. Non-linear ‘activation’
Multiple layers of network enables
1. Sophisticated features to be learned
Problem:
1. So many parameters - requires much more data
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ConvolutionalNeural Network
CNN1. Leverages spatio-temporal
relationships2. Applies discrete convolution
operations3. Kernels are the only trainable
parameters - reduce # parameters
4. Insights in kernels:
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Compressing and separatinginformation
The value of each pixel does not matter, the relationships among neighbouring pixels do.
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Challenges
1. Data quality (GIGO)2. Data quantity (overfitting)3. Black box (neural networks)4. Feature engineering (for shallow learning)5. Domain expertise6. No guarantee
Unsupervised Learning
“ Unsupervised machine learning algorithms infer patterns from data, without reference to known outcomes
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What is the underlying structure?
https://www.bbc.co.uk/news/science-environment-47267081https://xkcd.com/1838/
Anomaly Detection
“ Can automatically discover unusual data points in your dataset
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Anomaly Detection: example
- Looking for misrecorded values
Credit: National Science Foundation
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Anomaly Detection: example
- Classifying extreme events
https://www.ncdc.noaa.gov/extremes/cei/definition
Clustering
“ Allows you to automatically split the dataset into groups according to similarity
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Clustering
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Clustering:Example
Antarctic ocean temperature profiles
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Clustering:Example
Antarctic ocean temperature profiles
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Clustering:Example
Antarctic ocean temperature profiles
Latent Variable Models
“ Decomposing the dataset into multiple components
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Latent Variable Models
Non‑random correlation structures and dimensionality reduction in multivariate climate data - Martin Vejmelka et al. 36
Latent Variable Models: Example
Component Analysis
Autoencoder
“ Tries to learn how to compress data down to the most important components
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Autoencoder:Basic Structure
Image Credit: https://www.jeremyjordan.me/autoencoders/
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Autoencoder:Application
Dimensionality reduction and finding ‘extreme’ weather events
Topic Modelling
“ Latent variable models applied to text to boost your literature searches
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Topic Modelling: Example
Finding the topics of active research and research network
structure
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Causal Inference
“What are the dynamics of the system? What drives what?
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Causal Inference: Example
Finding direct and indirect teleconnections
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Generative Adversarial Networks
“ Learns the distribution function of data so that you can draw more unique samples
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GANs:What they do- Generator takes random
input and tries to create fake image
- Discriminator tries to tell difference between real and fake images
https://thispersondoesnotexist.com/
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GANs: Example
Generating new, unique examples using what the
network has discovered about the data set
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GANs: Example
Used to emulate a simulator
Opportunities/resources
“ In data science, 80% of the time is spent preparing data with the remaining 20% spent complaining about the need to prepare data...
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Questions?
Gaussian Processes
“ Fit a statistical model with minimum assumptions which will return a value and an uncertainty in that value
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Gaussian Processes:
problem
Gaussian Processes:
problem
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Gaussian Processes:
problem
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Gaussian Processes:
example
Fitting model to fill gaps in data
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Gaussian Processes:
exampleOptimising an expensive
experiment or physical model
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Gaussian Processes:
example
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