Update for 26-02-22 22:45

tech/algorithms.wiki

@@ -56,6 +56,7 @@ Different ways to store and operate on data, with differing efficiency
 * [[randomized_algorithm|Randomized Algorithms]]
 * [[genetic|Genetic Algorithms]]
 * [[swarm|Swarm Inteligence]]
+* [[machine_learning|Machine Learning]]
 * [[neural|Neural Networks]]
 
 == Common operations ==

tech/divide_and_conquer.wiki

@@ -0,0 +1 @@
+= Divide and conquer =

tech/dynamic_programming.wiki

@@ -8,11 +8,28 @@ have the following properties
 2) An optimal solution can be formed from optimal solutions to the overlapping
    subproblems derrived from the original problem.
 
-There are two ways to implement a DP algo
+Generally, problems requiring a DP algo ask for the optimum value (min/max), or
+the number of ways to do something *AND* future/larger descisions depend on
+earlier descisions. If they do not depend on earlier descisions, then consider
+a [[greedy_algorithm]].
+
+There are two ways to implement a DP algo:
 
 == Implimentation ==
 
 === Bottom up (Tabulation) ===
 
 For tabulation we start at the lowest sub problem and work our way up to the
-desired solution.
+desired solution. Generally bottom up is the fastest way to implement a dp
+algo, as there is no overhead of recursion.
+
+Usually implemented as iteration of the problem space
+
+=== Top down (Memoization) ===
+
+For memoization we start at the problem we would like to solve then descend
+into the lower and lower subproblems, using a system to store the results of
+our compuations as we descend. This is to ensure we do not do any unnecassy
+computations. T
+
+Usually implemented as recurssion

tech/machine_learning.wiki

@@ -0,0 +1,3 @@
+= Machine Learning =
+
+Machine learning