Useful CS classes for an Applied Math major

[poorasian]

I have to take any 3 CS electives for my Computational Mathematics major. I'm thinking about Algorithms, AI, and Database Systems. I can take more if I want to.

Are there any particularly useful CS classes? I don't really have a career in mind right now.

 

[chiro]

I have to take any 3 CS electives for my Computational Mathematics major. I'm thinking about Algorithms, AI, and Database Systems. I can take more if I want to.

Are there any particularly useful CS classes? I don't really have a career in mind right now.

Hey poorasian and welcome to the forums.

For applied mathematics, I would recommend any kind of elective that deals with numerical algorithms including numerical optimization and anything involving matrix methods to solve problems.

Database subjects are also good because in applied situations you often have to source your data from a data source such as a database. By knowing how to grab your data and get it into the form that your program needs, you can solve that puzzle so that you can focus on what you need to do.

I'd also recommend algorithms as well because if what you are going to do is computationally intensive (in terms of computational complexity), then it will make a huge difference using specific algorithms over others especially if you need a lot of computer power.

Anything with any kind of numerical methods is something I definitely recommend if you think about it in the context of applied mathematics.

 

[feuxfollets]

Machine learning is a very useful thing to know. There's some very interesting ideas there, and the field also has lots of applications.

An AI course heavily depends on how it is taught at your university. Most undergraduate AI courses don't really go into much mathematical detail. Honestly most of the algorithms taught in intro AI courses are really "intuitive" and you can easily pick up yourself by glancing over a wikipedia page, it's probably not worth taking a course on it.

 

[chiro]

Machine learning is a very useful thing to know. There's some very interesting ideas there, and the field also has lots of applications.

An AI course heavily depends on how it is taught at your university. Most undergraduate AI courses don't really go into much mathematical detail. Honestly most of the algorithms taught in intro AI courses are really "intuitive" and you can easily pick up yourself by glancing over a wikipedia page, it's probably not worth taking a course on it.

Just to add to this, I would suggest that if the OP wanted to pursue this in one form or another, that they at least be acquainted with the ideas and applications of probability and statistics since many of these methods are in fact statistical and have particular interpretations of the aims of the algorithms in this context.

 

[poorasian]

Hi, I've taken 2 courses on probability theory and going to take a mathematical statistics course. I really enjoy the subject and the kind of thinking required to solve those kinds of problems.

The AI course description reads: "The first couple lectures review the LISP programming language. The next part of the course will cover problem solving including problem spaces, brute-force and heuristic search, two-player games, constraint-satisfaction problems, and planning techniques. The third section will deal with knowledge representation including predicate calculus, non-monotonic inference, probabilistic reasoning, production systems, semantic nets, frames, scripts, and semantic primitives. Finally, there will be several lectures dealing with specialized topics such as expert systems, natural language processing, speech, vision, and neural networks."

I've taken numerical methods through the mathematics department. Not my favorite subject but I can see how useful it is.

Machine learning appears to only be offered at the graduate level. Oh well.

 

[tcbh]

Is it possible that graduate level courses will count toward your major?