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This is a trivial question - but something I always miss in the day-to-day programming.
Is there a gook lookup reference available for the common algorithms that we usually face in our everyday programming - sorting,sequences,graphs.
The emphasis is more on the applicability and pseudocode ,rather than the mathematical proofs(which I find is what books tend to stress on).
The idea is to keep a ready reference,as and when we need to resort to one of these algorithms into our respective development project and languages.
Dictionary of Algorithms and Data Structures
How about this?
List of algorithms#Wikipedia
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Is it necessary to memorize the code of data structures like linked lists, dynamic arrays , circular linked list, queues , stacks , Graphs etc. Or just the basic knowledge of code is enough ? What kind of questions can be asked in a job interview regarding data structures ?
I don't know what your (future) employer may ask, but generally, I'd say no. You have to know how they work and what they're used for, expecially which data structure serves which purpose with its advantages/disadvantages. If you know that, you'll be able to write the code of such a structure without having it memorized - because you know how it will work.
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Can any algorithm that performs automatic learning be called a "machine learning algorithm"? Or is this designation is reserved to the known ML algorithms like SVM, Feature Selection... ?
Any algorithm that learns to do a task by itself and gets better at it is considered machine learning even if it just as simple as computing the joint probability. Only condition is automated learning, that's all.
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I'm researching on compression algorithms (huffman coding and LZ77) and was wondering how I would evaluate their efficiency depending on the input image. I know how they work but I can't find information on their evaluation (mathematically). Thanks!
General-purpose (universal) compressors like LZ77 are usually compared by testing them against a standard set of sources and comparing the results, see: http://www.maximumcompression.com/, http://www.maximumcompression.com/data/summary_mf.php, for example.
Compressors for specific purposes are tested against source sets that are chosen to be as representative as possible.
For some applications it is also useful to place mathematical bounds on compression efficiency in terms of the source entropy.
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I have seen people code for example, say a quicksort algorithm without using OOP. I have also seen the identical algorithm in Robert Lafore's data structure and algorithm textbook written under OOP framework.
Is it better to do it using OOP ? What are the advantages or disadvantages?
Suppose I know how to write an algorithm in both ways, should I always do it the OOP way ? Or does it depend on the algorithm itself ? If yes, then what algorithms would using OOP be beneficial ?
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Scheme lists are slow for random access, which is a common operation in many applications such as image processing. Does this make it naturally handicapped for that kind of application?
If performance is a concern, then you should definitely consider using fixed-access-time structures. Fortunately, Scheme has lots of these, too. The "vector" is the simplest one; it's a close match to what most languages call an "array".