Introduction to Distributed Systems
14:36Distributed Systems 1.3: RPC (Remote Procedure Call)
19:44The Column Space of A Contains All Vectors Ax
52:14Multiplying and Factoring Matrices
48:25Orthonormal Columns in Q Give Q'Q = I
49:24Eigenvalues and Eigenvectors
48:55Positive Definite and Semidefinite Matrices
45:27Singular Value Decomposition (SVD)
53:33Broadcast Ordering
16:19Distributed Systems 2.3: System models
20:44Replication
25:21Consensus
18:15Raft
38:09Two-Phase Commit
18:45Linearizability
18:44Eventual Consistency
14:59Collaboration Software
36:26Distributed Systems 3.1: Physical time
20:47Distributed Systems: Google's Spanner
18:41Distributed Systems 3.2: Clock synchronisation
15:57Distributed Systems 3.3: Causality and happens-before
16:24Distributed Systems 4.1: Logical time
24:01The Column Space of A Contains All Vectors Ax
MathWorks Professor of Mathematics
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018.
In this first lecture, Professor Strang introduces the linear algebra principles critical for understanding the content of the course. In particular, matrix-vector multiplication Ax and the column space of a matrix and the rank.
Distributed Systems 1.3: RPC (Remote Procedure Call)
Next · Lecture 4Multiplying and Factoring Matrices