Lectures related to Information Retrieval Indexing: Part 2

Explores data-parallel programming with vector processors and SIMD, and introduces MapReduce, Pregel, and TensorFlow.

Covers the analysis of functions, composition, and mathematical induction.

Explores counterfactuals in SEMs and D-Separation in graphical models.

Covers the instructor's work on designing, building, scaling, and ensuring the robustness of operating systems during their PhD.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Explores predicting reaction yields with deep learning models and the importance of high-quality data sets in chemistry.