Basics

Linear Regression

Logistic regression-1

Logistic regression-2

Quality Metrics-1

Quality Metrics-2

Decision trees-1

Ensembles-1

Ensembles-2

Open flow discussion

Useful hacks.

Clustering-1

Clustering-2

Intro to NN

Intro to Pytorch

CNN

Network regularization

Autoregressive networks

Autoregressive networks-2

Network architectures: tips and tricks

Intro

Full Bayesian Inference

Bayesian linear regression

Variational Inference

Gaussian Processes

Bayesian Neural Networks

Bayesian Neural Networks

VAE

Seminar: VAE

Generative models

Seminar: Practice on basic generative models

Introduction to distances Pt. 1

Seminar: Distances Pt. 1

Introduction to distances Pt. 2

Seminar: Distances Pt. 2

Autoencoders

GANs

Advanced GANs

Flow models

Invertible Generative Models

Introduction to black-box optimization

Variational Optimization

Bayesian Optimization

BO-GP and friends-1

BO-GP and friends-2

Open flow discussion

Learning to pivot

Deep Learning at scale

Interpretability

Anomaly Detection-1

Anomaly Detection-2

Student's project presentations

Follow-up (VR?) party