19EE2049 Industrial Practice V (FPGA Implementation for real-time applications) 0:0:1 0.5

Module 5: Physics of Wind Power (5 Hours)

History of wind power, Indian and Global statistics, Wind physics, Betz limit, Wind speed statistics probability distributions, Wind speed and power-cumulative distribution functions

Module 6: Description of Forecasting Techniques (12 Hours)

Time Series and Cross-sectional Data, Measuring Forecast Accuracy, Principles of Decomposition, Averaging Methods, Smoothing Methods, Regression moving average models, ARIMA Models, Recurrent Neural Network.

Text Book

1. Makridakis S., S. C. Wheelwright, R.J. Hyndman, “Forecasting – Methods and Applications”, 3rd Edition, Wiley-India Edition, Delhi, 2011

Reference Books

1. Wind Resource Assessment Handbook, AWS Scientific Inc., New York 1997. 2. Michael Brower, Daniel W. Bernadett, Kurt V.Elsholz, Matthew V. Filippelli, Michael J. Markus, Mark A. Taylor, Jeremy Tensen, “Wind Resource Assessment: A Practical Guide to Developing a Wind Project”, John Wiley & Sons, London, 2012. 3. Kleissl J., “Solar Energy Forecasting and Resource Assessment”, Academic Press, 1st Edition, 2013. 4. Richard Headen Inman, Jr., “Solar Forecasting Review”, M.S Thesis, University of California, San Diego, 2012.

19EE2045 Machine Learning Applications in Networking L T P C

3 0 0 3

Course Objectives:

1. To provide knowledge about the machine learning for wireless sensor networks. 2. To equip with required machine learning and deep learning skills in the field of wireless sensor networks. 3. To analyze and comprehend the various operating procedure involved in machine learning algorithms for Wireless sensor networks.

Course Outcomes:

At the end of this course students will demonstrate the ability to 1. Explain the basics of machine learning techniques. 2. Differentiate the unsupervised and semi supervised learning techniques 3. Know the wireless ad hoc and sensor networks 4. Apply the machine learning for wireless sensor networks 5. Analyze the machine learning algorithms for wireless sensor networks 6. Explain the integration of WSN and future applications of machine learning in WSN

Module 1: Introduction to Machine Learning (8 Hours)

Overview of machine learning - Supervised learning- Regression- Linear regression - polynomial regression, - locally weighted regression- Decision trees- Random forest - Gaussian parameter estimation - maximum likelihood estimation - Bayesian estimation, the perceptron algorithm multilayer perceptrons – backpropagation- Deep Learning-introduction, Support vector machine, kNearest neighbor,

Module 2: Introduction to Unsupervised and Semi supervised Learning (8 Hours)

Unsupervised learning, k-means clustering, Hierarchical clustering Fuzzy-c-means clustering, Singular value decomposition, Principle component analysis Independent component analysis, Semi-supervised learning-Reinforcement learning Module 3: Wireless, Adhoc and Sensor Networks (8 Hours) Evolution of Wireless Networks – Overview of various Wirelesses -Networks System Architecture 802.11 MAC - Wireless standards- IEEE standards 802.11a/b/g/n comparison, Ad hoc Networks issues and applications – Routing in Ad hoc networks - Sensor Networks

Module 4: Machine Learning for WSN (7 Hours)

Machine learning for wireless sensor networks, advantages, features, limitations, applications of machine learning in wireless sensor networks