20
Markov Chains: Hopping Around
Lecture no. 20 from the course: Mastering Linear Algebra: An Introduction with Applications
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Taught by Professor Francis Su | 33 min | Categories: Default Category
The algorithm for the Google search engine is based on viewing websurfing as a Markov chain. So are speech-recognition programs, models for predicting genetic drift, and many other data structures. Investigate this practical tool, which employs probabilistic rules to advance from one state to the next. Find that Markov chains converge on at least one steady-state vector, an eigenvector.
24 Lectures
1
Linear Algebra: Powerful Transformations
0
of 28 min
2
Vectors: Describing Space and Motion
0
of 27 min
3
Linear Geometry: Dots and Crosses
0
of 28 min
4
Matrix Operations
0
of 31 min
5
Linear Transformations
0
of 28 min
6
Systems of Linear Equations
0
of 28 min
7
Reduced Row Echelon Form
0
of 28 min
8
Span and Linear Dependence
0
of 31 min
9
Subspaces: Special Subsets to Look For
0
of 29 min
10
Bases: Basic Building Blocks
0
of 29 min
11
Invertible Matrices: Undoing What You Did
0
of 30 min
12
The Invertible Matrix Theorem
0
of 34 min
13
Determinants: Numbers That Say a Lot
0
of 30 min
14
Eigenstuff: Revealing Hidden Structure
0
of 27 min
15
Eigenvectors and Eigenvalues: Geometry
0
of 29 min
16
Diagonalizability
0
of 32 min
17
Population Dynamics: Foxes and Rabbits
0
of 30 min
18
Differential Equations: New Applications
0
of 33 min
19
Orthogonality: Squaring Things Up
0
of 32 min
20
Markov Chains: Hopping Around
0
of 33 min
21
Multivariable Calculus: Derivative Matrix
0
of 31 min
22
Multilinear Regression: Least Squares
0
of 28 min
23
Singular Value Decomposition: So Cool
0
of 32 min
24
General Vector Spaces: More to Explore
0
of 34 min
