![SOME TERMINOLOGY
• Inputs vector X= (x1, x2, x3, …, xm , t)
• Weights (wi,j) , are the weighted connections
between nodes i and j. Weight matrix W=[wi,j]
• Outputs (yj): yj j=1 to n (No.of points),
– Model y(x,W)
• Targets : vector t, tj , tj j=1 to n.
• Activation Function: For neural networks, g(·) is a
mathematical function that describes the firing of the neuron
as a response to the weighted inputs, such as the threshold
function described.
• Error E: difference in outputs y and targets t](https://image.slidesharecdn.com/unit-1-230413063212-2e888d55/85/Unit-1-ppt-22-320.jpg)
Unit-1.ppt
- 1. PC603IT Machine Learning Instructor: Dr. A Srinivas Reddy
- 2. IF DATA HAD MASS, THE EARTH WOULD BE A BLACK HOLE • Computers capture and store terabytes of data every day. – Data from shops, banks, hospitals, scientific laboratories, and many more. – Data of MRI, DNA, Social networks, sales Transactions, etc. • Challenge: How to use this data to and make human file easy, earn business, etc. • Machine learning solves these problems.
- 3. Why not humans? • Complexity of data: Volume, Variety and Velocity, high dimension data. • Humans cannot handle such complex data. • Cannot understand • Cannot process and • Cannot analyze
- 4. Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program
- 5. Magic? No, more like gardening • Seeds = Algorithms • Nutrients = Data • Gardener = You • Plants = Programs
- 6. LEARNING • Humans learn from experience. – remembering, adapting, and generalizing, reasoning, and logical deduction. • Machines is learning from data. • How? – Modify or adapt their actions to fit the data. – Example: Human vs computer game. Computer starts learning from humans and finally plays better than human after certain number of trails.
- 7. • Data mining: Extraction of useful information (in the form of patterns) from massive datasets. • Machine learning: Classification, clustering, outlier detection, etc. • Machine learning: Includes training over data and testing on data. • Training: Learning from data • Testing: Measuring the performance of model • ML Complexity: – Training and testing complexity should be low – Can be deployed on edge devices.
- 8. Types of Learning • Supervised (inductive) learning – Training data includes desired outputs. • Unsupervised learning – Training data does not include desired outputs. • Reinforcement learning – Rewards from sequence of actions. • Evolutionary learning – Biological evolution can be seen as a learning process.
- 9. 9 Supervised Machine learning • Like human learning from past experiences. • A computer does not have “experiences”. • A computer system learns from data, which represent some “past experiences” of an application domain. • Our focus: learn a target function that can be used to predict the values of a discrete class attribute, e.g., approve or not-approved, and high-risk or low risk. • Supervised learning task: regression, classification, or inductive learning.
- 10. 10 What do we mean by learning? • Given – a data set D, – a task T, and – a performance measure M, a computer system is said to learn from D to perform the task T if after learning the system’s performance on T improves as measured by M. • In other words, the learned model helps the system to perform T better as compared to no learning.
- 11. 11 An example: data (loan application) Approved or not
- 12. 12 An example • Data: Loan application data • Task: Predict whether a loan should be approved or not. • Performance measure: accuracy. No learning: classify all future applications (test data) to the majority class (i.e., Yes): Accuracy = 9/15 = 60%. • We can do better than 60% with learning.
- 13. Regression • Regression problem: Fit a mathematical function describing a curve, so that the curve passes as close as possible to all the datapoints. • It is generally a problem of function approximation or interpolation, working out the value between values that we know • Given a dataset <Xi, ti> of N points. • What is the function in x, t fits all the points. • ax3 + bx2 + cx + d = 0, • t = 3 sin(5x)
- 14. Regression
- 15. 15 • Data: A set of data records (also called examples, instances or cases) described by – k attributes: A1, A2, … Ak. – a class: Each example is labelled with a pre- defined class. • Goal: To learn a classification model from the data that can be used to predict the classes of new (future, or test) cases/instances. Classification: Data and the goal
- 16. 16 An example: the learning task • Learn a classification model from the data • Use the model to classify future loan applications into – Yes (approved) and – No (not approved) • What is the class for following case/instance?
- 17. 17 Supervised vs. unsupervised Learning • Supervised learning: classification is seen as supervised learning from examples. – Supervision: The data (observations, measurements, etc.) are labeled with pre- defined classes. It is like that a “teacher” gives the classes (supervision). – Test data are classified into these classes too. • Unsupervised learning (clustering) – Class labels of the data are unknown – Given a set of data, the task is to establish the existence of classes or clusters in the data
- 18. 18 Supervised learning process: two steps Learning (training): Learn a model using the training data Testing: Test the model using unseen test data to assess the model accuracy , cases test of number Total tions classifica correct of Number Accuracy
- 19. • How do we classify these coins • Select the list of features. • What happens if there are to many features? – the number of datapoints required increases
- 20. 20 Fundamental assumption of learning Assumption: The distribution of training examples is identical to the distribution of test examples (including future unseen examples). • In practice, this assumption is often violated to certain degree. • Strong violations will clearly result in poor classification accuracy. • To achieve good accuracy on the test data, training examples must be sufficiently representative of the test data.
- 21. THE MACHINE LEARNING PROCESS • A process by which machine learning algorithms can be selected, applied, and evaluated for the problem. • Data Collection and Preparation • Feature Selection • Algorithm Choice • Parameter and Model Selection • Training • Evaluation
- 22. SOME TERMINOLOGY • Inputs vector X= (x1, x2, x3, …, xm , t) • Weights (wi,j) , are the weighted connections between nodes i and j. Weight matrix W=[wi,j] • Outputs (yj): yj j=1 to n (No.of points), – Model y(x,W) • Targets : vector t, tj , tj j=1 to n. • Activation Function: For neural networks, g(·) is a mathematical function that describes the firing of the neuron as a response to the weighted inputs, such as the threshold function described. • Error E: difference in outputs y and targets t
- 23. Weight Space • Equation of a curve: w1x1 + w2x2 + w3x + d = 0 • Axes: mutually orthogonal