1. Overview of machine learning applications in biology 2. Machine Learning Methods I. Associations, II. Classification, III. Regression, IV. Unsupervised learning, V. Reinforcement learning, Introduction to the Machine Learning Models 3.

Model selection and generalization, 4. Multivariate Methods, 5. Dimensional Reduction, 6. Clustering (K-means, Adaptive Resonance Theory, Self Organizing Maps), 7. Kernel Machines, 8. Hidden Markov Model (HMM) 9. Neural nets and Deep Learning 10. Bayesian Theory for machine learning, 11.

Ethics in machine learning and artificial intelligence Using Machine learning methods in Life Sciences 12. Different Machine learning models and their appropriate usages 13. Machine learning and its use in understanding Life Sciences, 14. Supervised and unsupervised learning, neural networks and deep learning methods in Biology 15. Recognizing phenotypes using machine learning 16. Reinforcement learning and Support vector machines and random forests in Biological processes Machine Learning: Software and Applications used in Biology and Medicine 17. The Cloud, Microsoft, Google, Facebook applications in healthcare 18. Applications and software of machine learning and artificial intelligence in medical knowledge in One Health 19.

Medical Health Approaches cloud set up, 20. Life Sciences in Azure and Amazon Web Services Application of ML in detection of Toxicity 21. Toxicity: An Introduction (drug toxicity and molecule-molecule interactions) 22. Machine learning and Toxicity Studies Application in Human life 23. Applications of machine learning in study of cell biology, 24. Genetics using unsupervised learning methods such as KNN, 25. Cell Fate analysis using PCA or similar dimensionality reduction methods, 26. Detection of disease through biomarker data and image analysis Application in Animal sciences 27.

Animal Behaviour: An Introduction 28. Study of animal behaviour by conventional methods and bottlenecks and advantages of machine learning 29. Machine learning and study of precision animal agriculture and animal husbandry 30. Machine learning in the study of animal health and veterinary sciences 31. Machine learning in identification of animal viral reservoirs. Application in Plants 32. Problems in Plant Biology that are yet to be tackled 33. Machine learning in agriculture, 34.

Machine learning in understanding of plant pathogen interactions, 35. Machine learning in plant disease research. Challenges and Road Ahead 36. BioRobotics A. An Introduction B. BioRobots in detection, identification, prevention and treatment of disease at molecular level 37. The challenges to application of machine learning in biological sciences 38. The future of machine learning.