Machine learning presentation (razi)
- 1. Self Introduction Name: Rizwan Shaukat (Razi) Major: MS(Software Engineering) ID: 2019272110006
- 2. Introduction to Machine Learning Basics of Machine Learning
- 3. About Subfield of Artificial Intelligence (AI) Name is derived from the concept that it deals with “construction and study of systems that can learn from data” Can be seen as building blocks to make computers learn to behave more intelligently It is a theoretical concept. There are various techniques with various implementations. http://en.wikipedia.org/wiki/Machine_learning In other words: “A computer program is said to learn from experience (E) with some class of tasks (T) and a performance measure (P) if its performance at tasks in T as measured by P improves with E”
- 4. Definitions “Learning is any process by which a system improves performance from experience.” (By Herbert Simon) Definition by Tom Mitchell (1998): Machine Learning is the study of algorithms that improve their performance P at some task T with experience E. A well-defined learning task is given by <P, T, E>. Machine learning investigates the mechanisms by which knowledge is acquired through experience Machine Learning is the field that concentrates on induction algorithms and on other algorithms that can be said to ``learn.''
- 5. When Do We Use Machine Learning ML is used when: Human expertise does not exist (navigating on Mars) Humans can’t explain their expertise (speech recognition) Models must be customized (personalized medicine) Models are based on huge amounts of data (genomics)
- 6. Terminologies Features The number of features or distinct traits that can be used to describe each item in a quantitative manner. Samples A sample is an item to process (e.g. classify). It can be a document, a picture, a sound, a video, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits. Feature vector is an n-dimensional vector of numerical features that represent some object.
- 7. Terminologies Conti…. Feature extraction Preparation of feature vector transforms the data in the high-dimensional space to a space of fewer dimensions. Training/Evolution set Set of data to discover potentially predictive relationships.
- 8. Lets go some what deeply… What do you mean by Apple
- 9. Learning (Training) Example of Apple:… Features: 1. Color: Radish/Red 2. Type : Fruit 3. Shape etc… Features: 1. Sky Blue 2. Logo 3. Shape etc… Features: 1. Yellow 2. Fruit 3. Shape etc…
- 10. Workflow
- 11. Growth of Machine Learning Machine learning is preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control Computational biology
- 12. Growth of Machine Learning Conti… This trend is accelerating Improved machine learning algorithms Improved data capture, networking, faster computers Software too complex to write by hand New sensors / IO devices Demand for self-customization to user, environment It turns out to be difficult to extract knowledge from human expertsfailure of expert systems in the 1980’s.
- 13. Applications Association Analysis Supervised Learning Unsupervised Learning Semi-Supervised Learning Reinforcement Learning
- 14. Learning Association Basket analysis: P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( chips | beer ) = 0.7 Market-Basket transactions TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 15. Supervised Learning the correct classes of the training data are known Credit: http://us.hudson.com/legal/blog/postid/513/predictive- analytics-artificial-intelligence-science-fiction-e-discovery-truth
- 16. Supervised Learning: Uses Prediction of future cases: Use the rule to predict the output for future inputs Knowledge extraction: The rule is easy to understand Compression: The rule is simpler than the data it explains Outlier detection: Exceptions that are not covered by the rule, e.g., fraud Example: decision trees tools that create rules
- 17. Unsupervised Learning Conti… the correct classes of the training data are not known Credit: http://us.hudson.com/legal/blog/postid/513/predictive- analytics-artificial-intelligence-science-fiction-e-discovery-truth
- 18. Unsupervised Learning Learning “what normally happens” No output Clustering: Grouping similar instances Other applications: Summarization, Association Analysis Example applications Customer segmentation in CRM Image compression: Color quantization Bioinformatics: Learning motifs
- 19. Semi-Supervised Learning A Mix of Supervised and Unsupervised learning Credit: http://us.hudson.com/legal/blog/postid/513/predictive- analytics-artificial-intelligence-science-fiction-e-discovery-truth
- 20. Reinforcement Learning allows the machine or software agent to learn its behavior based on feedback from the environment. This behavior can be learnt once and for all, or keep on adapting as time goes by. Credit: http://us.hudson.com/legal/blog/postid/513/predictive- analytics-artificial-intelligence-science-fiction-e-discovery-truth
- 21. Reinforcement Learning Topics: Policies: what actions should an agent take in a particular situation Utility estimation: how good is a state (used by policy) No supervised output but delayed reward Credit assignment problem (what was responsible for the outcome) Applications: Game playing Robot in a maze Multiple agents, partial observability, ...
- 22. Machine Learning Techniques
- 23. Techniques classification: predict class from observations clustering: group observations into “meaningful” groups regression (prediction): predict value from observations
- 24. Classification Example: Credit scoring Differentiating between low-risk and high-risk customers from their income and savings Discriminant: IF income > θ1 AND savings > θ2 THEN low-risk ELSE high-risk Model
- 25. Classification : Applications Pattern recognition Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style Character recognition: Different handwriting styles. Speech recognition: Temporal dependency. Use of a dictionary or the syntax of the language. Sensor fusion: Combine multiple modalities; eg, visual (lip image) and acoustic for speech Medical diagnosis: From symptoms to illnesses Web Advertising: Predict if a user clicks on an ad on the Internet.
- 26. Face Recognition In Face Recognition there is some raw data for the detection of any person’s mood. System will be trained through this data and it will give out put in percentages. Training examples of a person Test images
- 27. Clustering Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other objects are not predefined For e.g. these keywords “man’s shoe” “women’s shoe” “women’s t-shirt” “man’s t-shirt” can be cluster into 2 categories “shoe” and “t-shirt” or “man” and “women” Popular ones are K-means clustering and Hierarchical clustering
- 28. K-means Clustering partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. http://en.wikipedia.org/wiki/K-means_clustering http://pypr.sourceforge.net/kmeans.html
- 29. Hierarchical clustering method of cluster analysis which seeks to build a hierarchy of clusters. There can be two strategies Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Time complexity is O(n^3) Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. Time complexity is O(2^n) http://en.wikipedia.org/wiki/Hierarchical_clustering
- 30. Prediction : Regression is a measure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables (e.g. time and cost). regression analysis is a statistical process for estimating the relationships among variables. Regression means to predict the output value using training data. Popular one is Logistic regression (binary regression) http://en.wikipedia.org/wiki/Logistic_regression
- 31. Regression Application Navigating a car: Angle of the steering wheel (CMU NavLab) Kinematics of a robot arm α1= g1(x,y) α2= g2(x,y) α1 α2 (x,y)
- 32. Classification vs Regression Classification Classification means to group the output into a class. classification to predict the type of tumor i.e. harmful or not harmful using training data if it is discrete/categorical variable, then it is classification problem Regression Regression means to predict the output value using training data. regression to predict the house price from training data if it is a real number/continuous, then it is regression problem.
- 33. Popular Frameworks/Tools Weka Carrot2 Gate OpenNLP LingPipe Stanford NLP Mallet – Topic Modelling
- 34. Popular Frameworks/Tools Gensim – Topic Modelling (Python) Apache Mahout MLib – Apache Spark scikit-learn - Python LIBSVM : Support Vector Machines and many more…
- 35. Any Question?