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Introduction to-machine-learning

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This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.

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Introduction to-machine-learning

  1. 1. Introduction to Machine Learning Babu Priyavrat
  2. 2. Contents • What is Machine Learning? • Types of Machine Learning • Decision Tree and Random Forests • Neural Network • Deep Learning • Forecasting • Measuring Performance of ML algorithms • Pitfalls of Machine Learning
  3. 3. What is Machine Learning? • Definition by Arthur Samuel - Machine Learning is a technique which gives "computers the ability to learn without being explicitly programmed.“ • To mimic human intelligence or human learning process
  4. 4. Evolution of Machine Learning
  5. 5. How Humans learn? • Knowledge Transfer –attending lecture • Hit and Trial – learning cycling during your childhood What is actually happening? • Categorization • Prediction
  6. 6. We asked Machines to do same • Categorization • Prediction
  7. 7. Supervised Machine learning • Formal boring definition - Supervised learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). • Layman term – Make computers learn from experience • Task Driven
  8. 8. Supervised Learning
  9. 9. Example of supervised Machine Learning Categorization Categorizing whether tumor is malignant or benign Prediction (Regression) Predicting the house of price in given area
  10. 10. How is Supervised Learning Achieved? • The existing data is usually divided into training or testing set. For e.g., Training is usually 70% of data where as Testing is 30% of data. • Algorithm develops it model based on training data • Features important for model is usually selected by humans • Algorithm predicts the results for testing data and later the predicted value is compared with real value to give us accuracy. • Several algorithms are tried until required accuracy is achieved
  11. 11. UnSupervised Learning Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. It is data driven. Clustering a tumor of same kind but doesn’t know it’s nature.
  12. 12. UnSupervised Learning Examples Network Intrusion detection Clustering your customer base
  13. 13. Reinforcement Learning • Reinforcement learning is a setting where we have a sequential decision problem. Making a decision now influences what decisions we can make in the future. A reward function is provided that tells us how “good” certain states are. • For e.g., Making robot learn against worthy opponent to play table tennis
  14. 14. Reinforcement Learning Characteristics • No direct training examples – (delayed) rewards later • Need for exploration of environment or exploitation of environment • The environment might be stochastic and/or unknown • The actions of the learner affects future rewards
  15. 15. Machine Learning Process in Business
  16. 16. Decision Tree Decision is a simple representation for Classifying examples. Decision tree learning is one of the most successful techniques for supervised classification learning. For e.g., Surviving Titanic is famous first Machine Learning explanation for Decision Tree
  17. 17. Random Forest • Random Forest Tree is a Supervised Machine Learning Algorithm Based on Decision Trees. • It is Collective Decisions of Different Decision Trees. • In random forest, there is never a decision tree which have all features of all other decision trees.
  18. 18. Boosting • Form a large set of simple features • Initialize weights for training images • For T rounds • Normalize the weights • For available features from the set, train a classifier using a single feature and evaluate the training error • Choose the classifier with the lowest error • Update the weights of the training images: increase if classified wrongly by this classifier, decrease if correctly • Form the final strong classifier as the linear combination of the T classifiers (coefficient larger if training error is small)
  19. 19. Neural Network • a computer system modelled on the human brain and nervous system
  20. 20. Neural Network Example Predicting whether the person goes to Hospital In next 30 days based on historical Data ( Classification)
  21. 21. Neural Network Example
  22. 22. Mar I/O –Neural Network playing Mario ( Reinforcement Learning) https://youtu.be/qv6UVOQ0F44
  23. 23. Deep Learning
  24. 24. Applications of Deep Learning Facial Recognition
  25. 25. Applications of Deep Learning Auto-coloring can be achieved by Algorithmia API
  26. 26. Applications of Deep Learning https://youtu.be/FroRjEejA30 Semantic segmentation of street for Driverless Car
  27. 27. How Convolved kernel behaves!
  28. 28. A Cat!
  29. 29. Two layers of convolution, activation and pooling with 3 filter layer
  30. 30. Neural Network Zoo http://www.asimovinstitute.org/neural-network-zoo/
  31. 31. Forecasting • It is usually done on time-series data to predict the future trend • For e.g., forecasting Stock value based on historical data • Usually achieved by ARIMA (Auto- regressive integrated Moving Average) model Amazon stock forecast Amazon actual stock performance
  32. 32. Measuring performance of ML algorithms
  33. 33. Pitfalls of Machine Learning • Over fitting • Trying to make algorithm to work only for small set of data • Ignoring Human intuition • Forecasting usually fails because the algorithm is not able to gauge what humans consider valuable. • Machine Bias • For example, a 2015 study found that women were less likely to be shown high-income job ads by Google's AdSense and another study found that Amazon’s same-day delivery service was systematically not available in black neighborhoods, both for reasons that the companies could not explain, but were just the result of the black box methods they used • https://youtu.be/tleeC-KlsKA
  34. 34. Participate in Machine Learning Competitions http://www.kaggle.com/
  35. 35. Question & Answers
  36. 36. References • https://blogs.nvidia.com/blog/2016/07/29/what s-difference-artificial-intelligence-machine- learning-deep-learning-ai/ • https://blogs.nvidia.com/blog/2014/09/07/imag enet/ • https://www.linkedin.com/learning/machine- learning-essential-training-value-estimations • https://www.slideshare.net/drcfetr/an- overview-of-machine-learning • https://sflscientific.com/blog/2016/12/16/portfol io-prediction • https://ei.is.tuebingen.mpg.de/research_proje cts/robot-skill-learning • https://www.innoarchitech.com/machine- learning-an-in-depth-non-technical-guide-part- 2/ • http://erinshellman.github.io/data-mining- starter-kit/ • http://slideplayer.com/slide/7341084/ • http://machinelearningmastery.com/common- pitfalls-machine-learning-projects/ • https://www.togaware.com/datamining/surviv or/Neural_Network.html • https://read01.com/JNzJkL.html • http://www.papis.io/blog/introduction-to- multi-gpu-deep-learning-with-digits-mike-wang • https://hackernoon.com/visualizing-parts-of- convolutional-neural-networks-using-keras- and-cats-5cc01b214e59

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