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Machine Learning for Dummies
This document provides an introduction to machine learning, including: - It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks. - It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries. - It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
In this document
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An overview of Machine Learning covering its philosophy, advantages, applications, and algorithms.
Discussion on how the human brain and machines categorize and classify information using learning methods.
Introduction to learning algorithms and classification, emphasizing machine learning's role in image processing.
Highlights how ML streamlines processes and handles big data efficiently, automating tasks that require extensive training.Explains ML applications in sectors like banking, medicine, and computer interfaces including customer profiling.
Key components and examples of ML algorithms including output definitions and objective functions.
Introduction to Bayesian Networks for assessing probabilities and managing errors in machine learning processes.
Concepts of neural networks based on biological systems, focusing on their architecture and functionality.
Use of PCA and FA in simplifying data analysis by reducing dimensions while preserving essential information.
Introduction to SVM, discussing linear classifiers, margins, and support vectors for effective classification.
Differences between traditional models and machine learning approaches; highlights heuristic performance improvements.Detailed steps of K-Means clustering, emphasizing calculations, cluster fixing, and distance measures used in the process.
Acknowledgments and encouragement for further exploration in machine learning, including links to resources.
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Machine Learning for Dummies
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- 3. Machine Learning • Howdid our brain process the images? • How did the grouping happen? • Human brain processed the given images - learning • After learning the brain simply looked at the new image and compared with the groups classified the image to the closest group - Classification • If a machine has to perform the same operation we use Machine Learning • We write programs for learning and then classification, this is nothing but machine learning BigdataAnalytics VenkatReddy 3
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- 5. Example: Classification usingML • Image processing: • Machine learning can be used in classification Images & objects in an image • Ship • Water • Rock • Iron object • Fiber Object etc., • Does this really help? BigdataAnalytics VenkatReddy 5
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- 8. The main advantageof ML • Learning and writing an algorithm • Its easy for human brain but it is tough for a machine. It takes some time and good amount of training data for machine to accurately classify objects • Implementation and automation • This is easy for a machine. Once learnt a machine can process one million images without any fatigue where as human brain can’t • That’s why ML with bigdata is a deadly combination BigdataAnalytics VenkatReddy 8
- 9. Applications of MachineLearning • Banking / Telecom / Retail • Identify: • Prospective customers • Dissatisfied customers • Good customers • Bad payers • Obtain: • More effective advertising • Less credit risk • Fewer fraud • Decreased churn rate BigdataAnalytics VenkatReddy 9
- 10. Applications of MachineLearning • Biomedical / Biometrics • Medicine: • Screening • Diagnosis and prognosis • Drug discovery • Security: • Face recognition • Signature / fingerprint / iris verification • DNA fingerprinting BigdataAnalytics VenkatReddy 10
- 11. Applications of MachineLearning • Computer / Internet • Computer interfaces: • Troubleshooting wizards • Handwriting and speech • Brain waves • Internet • Hit ranking • Spam filtering • Text categorization • Text translation • Recommendation BigdataAnalytics VenkatReddy 11
- 12. Main Parts inMachine Learning process • Any Machine Learning algorithm has three parts 1. The Output 2. The Objective Function or Performance Matrix 3. The Input Email Spam Classification • The Output: Categorize email messages as spam or legitimate. • Objective Function: Percentage of email messages correctly classified. • The Input: Database of emails, some with human-given labels Hand Writing Recognition • The Output: Recognizing hand-written words • Objective Function: Percentage of words correctly classified • The Input: Database of human-labeled images of handwritten words BigdataAnalytics VenkatReddy 12
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- 14. Everything should bemade as simple as possible, but no simpler BigdataAnalytics VenkatReddy 14
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- 16. The Bayes Method A(2%)B(90%) C(8%) 1% 60% 2% • A, B, C Generate bolts, given a machine what is the probability that it will generate a defective bolt. • Now given a defective bolt, can we tell which machine has generated it? BigdataAnalytics VenkatReddy 16
- 17. Several Machine inSeveral Layers • Several intermediate machines • Each has its on error chance, This problem requires a Bayesian network to solve • Does C has anything to do if the bolt is finally coming out of F? A(20%) B(50%) C(30%) 1% 5% 2% D(60%) E(40%) F G H BigdataAnalytics VenkatReddy 17
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- 19. The Philosophy ofNeural Network • If we have to predict whether we win in a chess game • Can we use logistic regression? • Since there can be almost infinite number of intermediate steps its almost impossible to predict the probability of winning • If each move is considered as an input variable then there are almost (countable)infinite moves. A coefficient for each of them is always negligible. Neural network uses a simple technique, instead of predicting the final output they predict the intermediate output that lead to a win previously, this will be an input for predicting the next move BigdataAnalytics VenkatReddy 19
- 20. Biological Motivation forNN • Aartificial neural networks are built out of a densely interconnected set of simple units, where each unit takes a number of real-valued inputs (possibly the outputs of other units) and produces a single real-valued output (which may become the input to many other units). • The human brain is estimated to contain a densely interconnected network of approximately 1011 neurons, each connected, on average, to 104 others. • Neuron activity is typically inhibited through connections to other neurons. BigdataAnalytics VenkatReddy 20
- 21. ANN in SelfDriving Car BigdataAnalytics VenkatReddy 21
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- 23. Look at belowCricket Team Players Data Player Avg Runs Total wickets Height Not outs Highest Score Best Bowling 1 45 3 5.5 15 120 1 2 50 34 5.2 34 209 2 3 38 0 6 36 183 0 4 46 9 6.1 78 160 3 5 37 45 5.8 56 98 1 6 32 0 5.10 89 183 0 7 18 123 6 2 35 4 8 19 239 6.1 3 56 5 9 18 96 6.6 5 87 7 10 16 83 5.9 7 32 7 11 17 138 5.10 9 12 6 BigdataAnalytics VenkatReddy 23
- 24. Describe the players •If we have to describe or segregate the players do we really need Avg Runs, Total wickets, Height, Not outs, Highest Score, Best bowling? • Can we simply take • Avg Runs+ Not outs+ Highest Score as one factor? • Total wickets+ Height+ Best bowling as second factor? Defining these imaginary variables or a linear combination of variables to reduce the dimensions is called PCA or FA BigdataAnalytics VenkatReddy 24
- 25. Look at belowCricket Team Players Data Player Avg Runs Total wickets Height Not outs Highest Score Best Bowling 1 45 3 5.5 15 120 1 2 50 34 5.2 34 209 2 3 38 0 6 36 183 0 4 46 9 6.1 78 160 3 5 37 45 5.8 56 98 1 6 32 0 5.10 89 183 0 7 18 123 6 2 35 4 8 19 239 6.1 3 56 5 9 18 96 6.6 5 87 7 10 16 83 5.9 7 32 7 11 17 138 5.10 9 12 6 BigdataAnalytics VenkatReddy 25
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- 27. Linear Classifier Consider atwo dimensional dataset with two classes How would we classify this dataset? BigdataAnalytics VenkatReddy 27
- 28. Linear Classifier Both ofthe lines can be linear classifiers. BigdataAnalytics VenkatReddy 28
- 29. Linear Classifier There aremany lines that can be linear classifiers. Which one is the optimal classifier. BigdataAnalytics VenkatReddy 29
- 30. Classifier Margin Define themargin of a linear classifier as the width that theboundary could be increased by before hitting a datapoint. BigdataAnalytics VenkatReddy 30
- 31. Maximum Margin • Themaximum margin linear classifier is the linear classifier • with the maximum margin. • This is the simplest kind of SVM (Called Linear SVM) BigdataAnalytics VenkatReddy 31
- 32. Support Vectors • Examplesclosest to the hyper plane are support vectors. • Margin ρ of the separator is the distance between support vectors. ρw . x + b > 0 w . x + b < 0 w . x + b = 0 Support Vectors f(x) = sign(w . x + b) red is +1 blue is -1 BigdataAnalytics VenkatReddy 32
- 33. Difference Traditional Models& Machine Learning • ML is more heuristic • Focused on improving performance of a learning agent • Also looks at real-time self learning and robotics – areas not part of data mining • Some algorithms are too heuristic that there is no one right or wrong answer • Lets take K-Means example BigdataAnalytics VenkatReddy 33
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- 35. K-Means clustering Fix thenumber of clusters BigdataAnalytics VenkatReddy 35
- 36. K-Means clustering Calculate thedistance of each case from all clusters BigdataAnalytics VenkatReddy 36
- 37. K-Means clustering Re calculatethe cluster centers BigdataAnalytics VenkatReddy 37
- 38. K-Means clustering Continue tillthere is no significant change between two iterations BigdataAnalytics VenkatReddy 38
- 39. Calculating the distance Weight Cust168 Cust2 72 Cust3 100 Weight Age Cust1 68 25 Cust2 72 70 Cust3 100 28 Weight Age Income Cust1 68 25 60,000 Cust2 72 70 9,000 Cust3 100 28 62,000 Which of the two customers are similar? Which of the two customers are similar now? Which two of the customers are similar in this case? BigdataAnalytics VenkatReddy 39
- 40. Distance Measures • Euclideandistance • City-block (Manhattan) distance • Chebychev similarity • Minkowski distance • Mahalanobis distance • Maximum distance • Cosine similarity • Simple correlation between observations • Minimum distance • Weighted distance • Venkat Reddy distance • Not sure all these measures will result in same clusters in the above example. • So, same ML algorithms with different results. Generally this is not the case with conventional methods BigdataAnalytics VenkatReddy 40
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