Machine and Deep Learning Algorithms and Applications

Uday Shankar Shanthamallu received his Ph.D. degree in 2021 from the school of Electrical,Computer, and Energy Engineering at Arizona State University. He received his Master’s degree in electrical engineering from Arizona State University in 2018 and a Bachelor’s degree in electronics and communication engineering from the National Institute of Engineering, India,in 2011. His research interests include representation learning for graphs using machine learning and deep learning techniques. He also has experience on sensor data analytics for anomaly detection. His internship with NXP Semiconductors (2016) focused on algorithm development for sensor data analytics. He also interned with Lawrence Livermore National Laboratory during the summer of 2019 and 2020 where he built predictive models for human brain connectomes.Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University. He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (also an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, machine learning and sensor systems. He and his student team developed the computer simulation software Java-DSP and its award-winning iPhone/iPad and Android versions. He is author of two textbooks: Audio Processing and Coding by Wiley and DSP: An Interactive Approach (2nd ed.). He contributed to more than 350 papers, 11 monographs, 11 full patents, 10 provisional patents, and 12 patent pre-disclosures. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished Lecturer for the IEEE Signal Processing Society in 2004. He is a series editor for the Morgan & Claypool lecture series on algorithms and software. He received the 2018 IEEE Phoenix Chapter award with citation: “For significant innovations and patents in signal processing for sensor systems.” He also received the 2018 IEEE Region 6 Outstanding Educator Award (across 12 states) with citation: “For outstanding research and education contributions in signal processing.” He was elected recently to Senior Member of the National Academy of Inventors (NAI).

• Table of Contents:Preface.- Acknowledgments.- Introduction to Machine Learning.- Supervised Learning.- Unsupervised Learning.- Semi-Supervised Learning.- Neural Networks and Deep Learning.- Machine and Deep Learning Applications.- Conclusion and Future Directions.- Bibliography.- Authors' Biographies.