Machine Learning without the Math: An overview of Machine Learning

Machine Learning without the Math: An overview of Machine Learning

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  MACHINE LEARNING WITHOUT THEMATH ARSHAD AHMED MARCH 2016
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  CONTENTS • What ismachine learning? • Why do we need it? • Machine Learning Paradigms • Machine Learning Tasks • Desirable Properties • Common Problems • Summary and Conclusions
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  WHAT IS MACHINELEARNING? • Machine Learning is the study of computer algorithms that improve automatically through experience according to a performance measure • A lot of machine learning is a rediscovery of things that statisticians already knew. This is often disguised by differences in terminology: • Ridge regression = weight-decay • Fitting = learning • Held-out data = test data Why do this? “Instead of writing a program by hand, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job. This program may look very different from a typical hand-written program. It may contain millions of numbers. If we do it right, the program works for new cases as well as the ones we trained it on”. -- Geoffrey Hinton
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  WHY DO WENEED IT? • Simply put BIG DATA!
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  MACHINE LEARNING PARADIGMS •Supervised Learning • Unsupervised Learning • Reinforcement Learning
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  SUPERVISED LEARNING The algorithmis presented with training data which consists of examples which include both the inputs and the desired outputs, thus enabling it to learn a function. The learner should then be able to generalize from the presented data to unseen examples.
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  UNSUPERVISED LEARNING The algorithmis presented with examples from the input space only and a model is fit to these observations. For example, a clustering algorithm would be a form of unsupervised learning.
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  DEEP LEARNING EXAMPLE:CONVOLUTIONAL NEURAL NETWORKS (CONVNETS) A neural network method that is very powerful and works well with GPU computation paradigm. It is unsupervised because it does automatic feature extraction from raw input data.
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  REINFORCEMENT LEARNING An agentexplores an environment and at the end receives a reward, which may be either positive or negative. In effect, the agent is told whether he was right or wrong, but is not told how.
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  A CLASSIC MACHINELEARNING TASK
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  OTHER MACHINE LEARNINGTASKS • Pattern recognition • Medical Images • Facial Expressions • Spoken Word -> Digital Assistants • Pattern Generation • Generating images or motion sequences for example to fill a gap • Anomaly Detection • Network Intrusion Detection • Novelty Detection • Fraud Detection
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  DESIRABLE PROPERTIES • Simplesolutions are appropriately favoured over complicated ones. • Powerful enough to learn the solution to a given problem. • Stable to parameter variations. • Converges in finite time. • Scales reasonably with the number of training examples, the number of input features and the number of test examples.
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  COMMON PROBLEMS • Overfitting/ Under fitting • A trade off between complexity and good ness of fit of the model • Generalisation
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  SUMMARY AND CONCLUSIONS •We defined machine learning as consisting of supervised and unsupervised tasks • The purpose of this is to avoid tedious programming tasks and have the machine learn from examples. • It is a powerful tool with wide applications but understanding of the limitations and evaluation techniques are very important • The goal of any machine learning is to have a model with high accuracy and generalizability
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  THANK YOU FORLISTENING QUESTIONS?