Machine Learning

T V Geetha is a retired Senior Professor of Computer Science and Engineering with over 35 years of teaching experience in the areas of Artificial Intelligence, Machine Learning, Natural Language Processing and Information Retrieval. Her research interests include semantic, personalized and deep web search, semi-supervised learning for Indian languages, application of Indian philosophy to knowledge representation and reasoning, machine learning for adaptive e-learning, and application of machine learning and deep learning to biological literature mining and drug discovery. She is a recipient of the Young Women Scientist Award from the Government of Tamilnadu and Women of Excellence Award from Rotract Club of Chennai. She is a receipt of BSR Faculty Fellowship for Superannuated Faculty from University Grants Commission, Government of India for 2020-2023. S Sendhilkumar is working as Associate Professor in Department of Information Science and Technology, CEG, Anna University with 18 years of teaching experience in the areas of Data Mining, Machine Learning, Data Science and Social Network Analytics. His research interests include personalized information retrieval, Bibliometrics and social network mining. He is recipient of CTS Best Faculty Award for the year 2018 and awarded with Visvesvaraya Young Faculty Research Fellowship by Ministry of Electronics and Information Technology (MeitY), Government of India for 2019-2021.

1. Introduction. 2. Understanding Machine Learning. 3. Mathematiccal Foundations and Machine Learning. 4. Foundations and categoris of Machine Learning Techniques. 5. Machine Learning: Tool and Software 6. Classification Algorithms. 7. Probabilistic and Regression based approaches. 8. Performance Evaluation & Ensemble Methods. 9. Unsupervised Learning. 10. Sequence Models. 11. Reinforcement Learning. 12. Machine Learning Applications – Approaches. 13. Domain based Machine Learning Applications. 14. Ethical Aspects of Machine Learning. 15. Introduction to Deep Learning and Convolutional Neural Networks. 16. Other Models of Deep Learning and Applications of Deep Learning.