Lectures related to Untitled | EPFL Graph Search

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Covers the challenges and techniques of parameter estimation in wireless receivers.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Explores data manipulation, exploration, and visualization in data science projects using Python.

Explores recursive functions, including factorials and Fibonacci sequences, and their scope and namespaces.

Covers the Alfresco platform, focusing on document management and collaboration.

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.