Course outline: This is course on the basics of statistical inference / machine learning / data science. Emphasis is on learning the methods from first principles as opposed to using them as black boxes. We will use real biological examples as much as possible. We will be covering basics of probability, frequentist and Bayesian approaches, MCMC and model selection, unsupervised learning and several well-known supervised machine learning methods. Please see more details in last year's course flyer http://moodle.ncbs.res.in/pluginfile.php/34440/mod_resource/content/1/Sy...

Course outcome: Students will be learning basics of probability and differences between frequentist and Bayesian approaches. They will understand the basics of how machine learning methods work They will be able to implement many of the computational methods taught in class in Python/R. They will also be applying these methods to a real problem/dataset of their interest and see how these methods can be tuned to work in practice.