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| EEE 4-1 Intro to Machine Learning | ||
|---|---|---|
| S.No | Chapters / Units | Download Link |
| 1 | Unit 1 | Download |
| 2 | Unit 2 | Download |
| 3 | Unit 3 | Download |
| 4 | Unit 4 | Download |
| 5 | Unit 5 | Download |
EEE 4-1 Intro to Machine Learning Important Topics
Course Objectives:
Identify problems that are amenable to solution by Al methods, and which Al methods may be suited to solving a given problem. Formalize a given problem in the language/framework of different Al
methods (e.g., as a search problem, as a constraint satisfaction problem, as
a planning problem, as a Markov decision process, etc). Implement basic Al algorithms (e.g., standard search algorithms or dynamic programming).
• Design and carry out an empirical evaluation of different algorithms on problem formalization, and state the conclusions that the evaluation supports.
Course Outcomes:
After the completion of the course, student will be able to
Explain the definition and usage of the term 'the internet of things' in different contexts
Demonstrate on various network protocols used in IoT
Analyze on various key wireless technologies used in IoT systems, such as WiFi, 6LoWPAN, Bluetooth and ZigBee.
Illustrate on the role of big data, cloud computing and data analytics in IoT system
• Design a simple IoT system made up of sensors, wireless network connection, data analytics and display/actuators, and write the necessary control software
Unit I: Introduction: Towards Intelligent Machines Well posed Problems, Example of Applications in diverse fields, Data Representation, Domain Knowledge for Productive use of Machine Learning, Diversity of Data: Structured / Unstructured, Forms of Learning, Machine Learning and Data Mining, Basic Linear Algebra in Machine Learning Techniques.
Unit II: Supervised Learning: Rationale and Basics: Learning from Observations, Bias and Why Learning Works: Computational Learning Theory, Occam's Razor Principle and Over fitting Avoidance Heuristic Search in inductive Learning, Estimating Generalization Errors, Metrics for assessing regression, Metris for assessing classification.
Unit III: Statistical Learning: Machine Learning and Inferential Statistical Analysis, Descriptive Statistics in learning techniques, Bayesian Reasoning: A probabilistic approach to inference, K-Nearest Neighbor Classifier. Discriminant functions and regression functions, Linear Regression with Least Square Error Criterion, Logistic Regression for Classification Tasks, Fisher's Linear Discriminant and Thresholding for Classification, Minimum Description Length Principle.
Unit IV:
Support Vector Machines (SVM): Introduction, Linear Discriminant Functions for Binary Classification, Perceptron Algorithm, Large Margin Classifier for linearly seperable data, Linear Soft Margin Classifier for Overlapping Classes, Kernel Induced Feature Spaces, Nonlinear Classifier, and Regression by Support vector Machines. Learning with Neural Networks: Towards Cognitive Machine, Neuron Models, Network Architectures, Perceptrons, Linear neuron and the Widrow-Hoff Learning Rule, The error correction delta rule.
Unit V:
Multilayer Perceptron Networks and error back propagation algorithm, Radial Basis Functions Networks. Decision Tree Learning: Introduction, Example of classification decision tree, measures of impurity for evaluating splits in decision trees, ID3, C4.5, and CART decision trees, pruning the tree, strengths and weakness of decision tree approach.
Text Books:
1. Applied Machine Learning, 1st edition, M.Gopal, McGraw Hill Education, 2018
2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC) 1st Edition-2014
Reference Books:
1. Machine Learning Methods in the Environmental Sciences, Neural
Networks, William WHsieh, Cambridge Univ Press. 1 edition (August 31,
2009) 2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & SonsInc., 2nd Edition-2001
3. Chris Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
4. Machine Learning by Peter Flach, Cambridge-1st Edition 2012
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