## AS 2: Unsupervised Learning and Bayesian Statistics

Based on the popular Bayesian Stats course AM207 at Harvard, this course follows up on concepts of Bayesian Statistics from DS-2 to teach you how to reason about uncertainty in statistical models in a more principled way than in Frequentist statistics. We start by learning some information theory and a summary of frequentist methods. We move onto generative models and then to the generation of samples from Bayesian models to understand uncertainties and compute expectation values. We learn about Hamiltonian Monte Carlo and NUTS, state of the art sampling techniques with Physics origins which can be used to diagnose bad models. We then learn how to carry out Bayesian workflows and sensitivity analysis, culminating in the comparison and combination of models to make predictions with well defined uncertainty bounds.

• AI 1
• DSE 1
• DSE 2
• AI 2
• Probability Distributions, KL-Divergence, Deviance, Likelihood, and Likelihood ratios
• Generative Modeling, Gaussian Mixture Models, and EM
• Rejection Sampling and Importance Sampling
• MCMC, gibbs sampling,  and convergence tests
• Bayesian Regression
• Hierarchical Generalized linear models (GLMs)
• HMC, nuts and divergences
• Bayesian Workflow
• Bayesian Decision theory, Model Comparison
• Model Selection and Averaging.
• Gaussian Processes

After you finish this module, you will be able to carry out Bayesian computations used in a large number of fields where there is more structure to the problem than a Neural Network approximation. The Bayesian thinking that pervades Machine Learning and Deep Generative Models will become old hat for you, and you will be able to carry out principled models combining neural networks with domain knowledge, and providing uncertainty estimates, in many real world situations.

### Schedule

#### Students

##### Summer Cohort

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Conducted alongside elective

##### Winter Cohort

Intake 1:

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Conducted along with elective

Intake 2:

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Conducted along with elective

#### Working Professionals

##### Summer Cohort

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Conducted along with elective

##### Fall Cohort

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Conducted along with elective

##### Winter Cohort

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective