[Day 93] Node embeddings in graphs + some foundational statistics/math

 Hello :)
Today is Day 93!


A quick summary of today:
    • 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, inversion, EVD, SVD


Tomorrow, I believe I will continue with the 1.3 lecture from XCS224W: PageRank, Personalised PageRank and Matrix Factorization.


That is all for today!

See you tomorrow :)

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