50+ days of Machine Learning

 Hello :) Today is Day 93! A quick summary of today: covered lecture 1.2 Node embeddings on XCS224W: ML with Graphs from  Probabilistic Machine Learning: An Introduction , covered: chapter 5: Decision theory chapter 6: information theory chapter 7: linear algebra First, my notes from lecture 1.2 Node embeddings for graphs Covered topics: node embeddings: encoder and decoder, random walk, unsupervised feature learning, random walk optimization, negative sampling, node2vec, anonymous walks, learning walk embeddings Next, from Probabilistic ML: An introduction by Kevin Murphy Chapter 5: Decision theory Covered topics: classification problems, ROC curve, Precision-Recall curves, F-scores, Regression problems Chapter 6: Information theory Covered topics: entropy, entropy of discrete random variables, cross entropy, conditional entropy, perplexity Chapter 7: Linear algebra Covered topics: notations(vectors, matrices, tensors, vector spaces, linear map, properties), matrix multiplication, inv