#### Mutual Information

In probability theory and information theory, the mutual information (sometimes known by the archaic term transinformation) of two random variables is a quantity that measures the mutual dependence of the two random variables. The most common unit of measurement of mutual information is the bit, when logarithms to the base 2 are used.

via Mutual information – Wikipedia, the free encyclopedia.

#### Big-O Algorithm Complexity Cheat Sheet

Notation for asymptotic growth

- (theta) Θ upper and lower, tight[1] equal[2]
- (big-oh) O upper, tightness unknown less than or equal[3]
- (small-oh) o upper, not tight less than
- (big omega) Ω lower, tightness unknown greater than or equal
- (small omega) ω lower, not tight greater than
[1] Big O is the upper bound, while Omega is the lower bound. Theta requires both Big O and Omega, so that’s why it’s referred to as a tight bound (it must be both the upper and lower bound). For example, an algorithm taking Omega(n log n) takes at least n log n time but has no upper limit. An algorithm taking Theta(n log n) is far preferential since it takes AT LEAST n log n (Omega n log n) and NO MORE THAN n log n (Big O n log n).SO

[2] f(x)=Θ(g(n)) means f (the running time of the algorithm) grows exactly like g when n (input size) gets larger. In other words, the growth rate of f(x) is asymptotically proportional to g(n).

[3] Same thing. Here the growth rate is no faster than g(n). big-oh is the most useful because represents the worst-case behavior.

via Big-O Algorithm Complexity Cheat Sheet.

#### Asscociation Analysis (e.g. Market-Basket Analysis)

**Rule evaluation metrics**

- Support (s): fraction of contexts that contain both X and Y
- Condence (c): Measures how often the items in Y occur in contexts containing X

http://www.eecs.qmul.ac.uk/~christof/html/courses/ml4dm/week10-association-4pp.pdf

**Matrix Norms
**

The norm of a square matrix A is a non-negative real number denoted A – which is a measure of the magnitude of the matrix. There are several diﬀerent ways of deﬁning a matrix norm

- 1st Norm (= maximum of the absolute column sums)
- ….
- The Euclidean norm (= square root of the sum of all the squares of the elements)

http://www.personal.soton.ac.uk/jav/soton/HELM/workbooks/workbook_30/30_4_matrx_norms.pdf