Networks

Advanced Algorithms

pagerankdpccollaborative‑filteringgame‑theorybayesiannetworksinternet

The content here-in has been influenced by Mung Chiang's Networked Life and Introduction to Algorithms by CLRS.

Graph, Network

PageRank

A* Search

Min-Cut / Max-Flow (Ford-Fulkerson, Edmonds-Karp)

Distributed Power Control

Machine Learning & Signal Processing

CNNs (Convolutional Neural Networks)

LSTMs (Long Short-Term Memory Networks)

Transformers (Attention Mechanism)

FFT (Fast Fourier Transform)

PCA & SVD

Twin-Tower Models

Recommender Systems

Bayesian Rankings (could also be considered as an ensemble)

Collaborative Filtering

Matrix Factorisation

Twin-Tower Neural Architecture

Compression & Optimisation

Huffman Encoding

Linear Programming (Simplex)

Backpropagation

Expectation-Maximisation

Probabilistic Data Structures

Bloom Filters

Monte Carlo Tree Search

Ensemble Architectures

Bagging (Random Forests)

Boosting (Gradient Boosting, AdaBoost)

Stacking / Blending (different to MoE (below), difference lies in how and when models are weighted or selected)

Mixture of Experts (soft and hard gating)

Cryptography & Security

RSA (Rivest-Shamir-Adleman)

Diffie-Hellman Key Exchange

AES (Advanced Encryption Standard)

SHA (Secure Hash Algorithm)

Elliptic Curve Cryptography (ECC)

String Algorithms

Knuth-Morris-Pratt (KMP)

Rabin-Karp

Boyer-Moore

Suffix Arrays / Suffix Trees

What every computer science major should know

by https://matt.might.net/
architecturenetworksosunixmathematicscryptographytesting

Here are my distillations of Matt's seminal blog post: What CS Majors Ought to Know.

I have removed the imperative matter, and left the declarative material; marking off that which I have completed.

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