Machine Learning Workshop, TSEC 2020
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The document outlines a workshop on machine learning, covering definitions and relationships of key concepts like AI, ML, and deep learning. It details the machine learning workflow, goals for practical application, and various learning methodologies including supervised and unsupervised learning. Additionally, it emphasizes the importance of evaluating model performance and provides instructions for setting up a Python environment for data science experiments.
![—John McCarthy, coined term ‘AI’[1]
“It is the science and engineering of
making intelligent machines, especially
intelligent computer programs. It is
related to the similar task of using
computers to understand human
intelligence, but AI does not have to
confine itself to methods that are
biologically observable.”](https://image.slidesharecdn.com/machinelearningworkshoptsec2020-201126133807/85/Machine-Learning-Workshop-TSEC-2020-9-320.jpg)
![—Merriam Webster Dictionary [2]
“The capability of a
machine to imitate
intelligent human
behavior.”](https://image.slidesharecdn.com/machinelearningworkshoptsec2020-201126133807/85/Machine-Learning-Workshop-TSEC-2020-10-320.jpg)
Machine Learning Workshop, TSEC 2020
- 1. Machine Learning What is the Machine learning? By Siddharth Adelkar, People’s Archive of Rural India Copyright
- 2. 1. A general idea of what these terms mean: AI, ML, Deep Learning, NLP ? 2. How do they relate to each other? 3. To look deeper into the Machine Learning workflow 4. To install the first basic Python infrastructure for data science experiments 5. To write our first ML program Goals of this workship
- 3. Agenda Let’s code!What is AI/ML? Workflow Game Your project What is machine learning? What are the steps in ML? Learning on the Skin MNIST Dataset Use ML in your own project
- 4. 01 04 Objectives Wall What are your objectives? AI Wall Your understanding of the buzzwords in AI 03 Project Wall Do you have an application in mind? 02 Game wall Let’s play an ML game
- 5. OKR Wall ● What is your Objective today? ● What are some key results you expect? Write on a stickie paste it.
- 6. Project Wall One Artificial Intelligence project idea that you want to implement.
- 7. AI Wall Draw a Venn Diagram ● Artificial Intelligence ● Machine Learning ● Natural Language Processing ● Digital Signal Processing ● Image Processing ● Statistics ● Deep Learning ● Neural Networks ● Blockchain ● Deepfakes
- 8. What is AI? Write on a stickie. Paste it. 01
- 9. —John McCarthy, coined term ‘AI’[1] “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
- 10. —Merriam Webster Dictionary [2] “The capability of a machine to imitate intelligent human behavior.”
- 11. Some intelligent behaviors? What does the mind do? Please write on your AI wall
- 12. Intelligent human behaviors? Learning Machine Learning Language Natural Language Processing Planning & Decisions Multi-agents, Robotics, Operations Research Recognition Sensory -- Visual, Audio Reasoning Logical Theorist, Symbolic Reasoning Thinking? Turing test
- 13. LEARNING ● Write on your wall ● Cases of learning/ Examples of learning
- 14. 3 specific characterizations of Learning Unsupervised Finding some "structure" - groups, dimensions, clusters in unlabeled data Supervised Learn to predict a characteristic of a thing, given its other labels Reinforcement Out of scope Humans can do these easily. Can they? How do humans do it? Let's play.
- 15. Classical programming vs Machine Learning Credit: “Deep Learning with Python”, François Chollet
- 16. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. Concluding Session 1 ● We conclude that Mind does a large number of things. ● AI constitutes identifying these and then mimicking them ● One of these is learning. But learning itself is very large, deep and enigmatic. ● ML focuses on specific types of learning
- 17. The Machine Learning Workflow Game Let’s play! 02
- 18. ● Your team is given 50 cards. Shuffle ● Roll the dice. This is the number of classes. ● Group the cards in to those many categories using your logic. ● Write down your logic on a stickie. Hide it. Write classes on back of card. ● This was unsupervised learning. You were the machine. You did the learning and you clustered the dataset. You are machine
- 19. ● Shuffle cards. Take out 10, 5 of these are Dev Set. 5 rest are Test Set. ● Now go to the stage. Face the audience. Audience is machine. ● Train the machine. You can't talk. You can only show the two sides of the card. ● Epoch is when you go through all cards. ● Too slow? Change batch size. Audience is Machine
- 20. ● Training data was not labelled. ● You did not know what you are interested in. ● The problem at hand was clustering in meaningful categories ● Machine discovered features ● Machine created Clusters ● You knew what you were interested in ● You knew the i/p and o/p of Training Data ● Problem at hand was -- given i/p, predict o/p ● Machine predicted given past experience. Conclusions Unsupervised Supervised
- 21. Why Dev Set? Bias - Variance Credit: MartinThoma, Wikimedia Foundation
- 22. Model Performance Quality? How do you evaluate model quality?
- 23. True/False X Positive/Negative Class = Yes Class = No Class = Yes True Positive False Negative Class = No False Positive True Negative Predicted labels True labels
- 24. Accuracy TP+TN/TP+ FP+FN+TN Simply, correct predictions to total predictions. “Wolf! Wolf! Wolf!” Accuracy = 100%
- 25. Precision TP/TP+FP Make sure that you don’t have too many false positives! “He does not have TB.” COSTLY mistake!
- 26. Recall TP/TP+FN Make sure you are not claiming too many False Negatives.
- 27. F1 Score 2*(Recall * Precision) / (Recall + Precision) When distribution is uneven, a useful composite metric
- 28. Confusion Matrix Class 1 Class 2 Class 3 Class 4 Class 1 Class 2 Class 3 Class 4 Predicted labels True labels
- 29. Concluding Session 2 ● ML Workflow 1. Preparing the dataset- structuring, cleaning 2. Creating the test, dev and train sets 3. Deciding on an architecture 4. Iterating on dataset 5. Output model 6. Test model on dev 7. Happy? No? Go back to step 3 8. Yes? Test on test set. Final quality. ● We learned how to test model quality
- 30. Buzzword Alert Remember the hype cycle. All great technologies with potential go through a hype cycle. Don’t fall prey to over optimism. Don’t think too pessimistically either.
- 31. 4. Anaconda Navigator > Install Jupyter Notebooks 5. On Kaggle, go to Kernels. Download the top two kernel codes. 6. Upload and open both on Jupyter 1. Download “SkinMNIST” data from Kaggle. 2. Anaconda (Python 3.7) 3. conda create -n keras python=3.7 numpy scipy keras matplotlib pandas seaborn pillow scikit-learn 03 Let’s hack!
- 32. 1. Go to http://skin.test.woza.work/ 2. Upload an image of a skin lesion 3. The model predicts likelihood of condition Model, what is it good for?
- 33. 1. Skin Lesion Analyzer + Tensorflow.js Web App by Marsh https://www.kaggle.com/vbookshelf/skin-lesion-analyzer-tensorflow-js-web-app 2. Step wise Approach : CNN Model (77.0344% Accuracy) by Manu Siddhartha https://www.kaggle.com/sid321axn/step-wise-approach-cnn-model-77-0344-accuracy 3. HAM100000 “SKIN MNIST” Data https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T Thank you kagglers
- 34. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. Thanks! Do you have any questions? siddharth@ruralindiaonline.org +91 869 287 2055 https://ruralindiaonline.org