You’re a thief with a knapsack that can carry 4 lb of goods, You have four items that you can put into the knapsack.

Stereo	Laptop	Guitar	Iphone
$3000	$2000	$1500	$2000
4lbs	3lbs	1lbs	1lbs

calculate the longest common substring between blue and clues, fosh compare with fish, fort, longest common substring is used for word wrap.
solution of longest common substring

Classification, categorization into a group.
Regression, predicting a response (like a number).
Feature extraction means converting an item (like a fruit or a user) into a list of numbers that can be compared.
Picking good features is an important part of a successful KNN algorithm.

Trees, self-banlance tree, B-trees, Red-black trees, Heaps, Splay trees.
Inverted indexes, a hash that maps words to places where they appear.
The Fourier transform, given a smoothie, the Fourier transform will tell you the ingredients in the smoothie.
Parallel algorithms, consider for overhead of managing the parallelism and load balancing.
Map Reduce, the map function and the reduce function.
Bloom filters, Bloom filters are probabilistic data structures. Bloom filters are great because they take up very little space.
- False positives are possible. Google might say, “You’ve already crawled this site,” even though you haven’t.
- False negatives aren’t possible. If the bloom filter says, “You haven’t crawled this site,” then you definitely haven’t crawled this site.
HyperLogLog, HyperLogLog approximates the number of unique elements in a set. Just like bloom filters, it won’t give you an exact answer, but it comes very close and uses only a fraction of the memory a task like this would otherwise take.
The SHA algorithms, a one-way hash, can’t convert those hashes back to the original, and it’s locality insensitive, opposite with Simhash.
Diffie-Hellman key exchange, private key and public key.
Linear programming, is used to maximize something given some constraints, such as the knapsack problem.

References