Stanford University

Data Mining What is Data Mining? Statistical Limits of Data Mining Things Useful to Know Outline of the book MapReduce and the New Software Stack Distributed File Systems MapReduce Algorithms Using MapReduce Extensions to MapReduce The Communication Cost Model Complexity Theory for MapReduce Finding Similar Items Applications of Near-Neighbor Search Shingling of Documents Similarity-Preserving Summaries of Sets Locality-Sensitive Hashing for Documents Distance Measures The Theory of Locality-Sensitive Functions LSH Families for Other Distance Measures Applications of Locality-Sensitive Hashing Methods for High Degrees of Similarity Mining Data Streams...

Vectors Linear Functions Norm and Distance Clustering Linear Independence Matrices Matrix Examples Linear Equations Linear Dynamical Systems Matrix Multiplication Matrix Inverses Least Squares Least Squares Data Fitting Least Squares Classification Multi-Objective Least Squares Constrained Least Squares Constrained Least Squares Applications Non-linear Least Squares Constrained Non-linear Least Squares Introduction to Applied Linear Algebra: Vectors, Matrices, and Least squares. Stephen Boyd; Lieven Vandenberghe. Stanford University; University of California, Los Angeles....

Introduction Probability Theory Bayesian Networks Undirected Models Learning Bayes Nets Exact Inference Message Passing Sampling MAP Inference Structured Prediction Parameter Learning Bayesian Learning Structure Learning Exponential Families Variational Inference Advanced Topics and Conclusions https://cs228.stanford.edu/. Stefano Ermon. Stanford University.

Basic Text Processing Edit Distance Language Modeling Naive Bayes and Text Classification Sentiment Analysis Logistic Regression Information Retrieval Vector Semantics, Neural Embeddings, Word2Vec Relation Extraction Chatbots Recommender Systems (Collaborative Filtering) Web Graphs, Links and PageRank Social Networks NLP For Social Good CS 124: From Languages to Information. JDan Jurafsky. Stanford University. web.stanford.edu .

The instructors of Home | Ethics of Technological Disruption have written a book: “System Error: Where Big Tech Went Wrong and How We Can Reboot” ( available on SPL ). The authors' main ideas: the question is not about values, but better/worse social outcomes; understanding the optimization mindset of the technologist is more helpful than understanding the underlying tech; government regulation can and has led to good outcomes; a libertarian approach, e....