ES 661 Probabilistic Machine Learning

Summary

• Instructor: Nipun Batra (nipun.batra@iitgn.ac.in)
• Teaching Assistants: Zeel B Patel, Sarth Dubey, Madhav Kanda, Haikoo Khandor
• Course Timings: Monday 330-450 PM IST and Thursday 2-3:20 PM IST
• Slack Invite corrected!

Main topics

1. Bayesian Inference
2. Estimation: Maximum Likelihood, Maximum a Posteriori, Full Bayesian
3. Sampling: Rejection Sampling, Monte Carlo, Specific Sampling Techniques (like Box Muller)
4. Approximate Inference: Variational Inference, Markov Chain Monte Carlo, Laplace Approximation
5. Models: Bayesian Linear, Logistic regression; Bayesian Neural Networks; Gaussian Processes; Probabilistic PCA
6. Applications: Bayesian Optimization, Active learning