Which maths specialization is most useful to an EECS major interested

[member2357]

I am an EECS major in first year of undergrad at university. I want to do a second major in mathematics but I have to choose a specialization. I am interested in machine learning and AI, finance, EE, CS etc.

The specializations my university offers are:

• pure mathematics
• applied mathematics.
• discrete mathematics and operations research
• statistics and stochastic processes

I have to chose only one of these specializations.

I am not interested in the pure maths specialization because I don't think it is useful to me. It involves a lot of real and complex analysis and abstract algebra.

I am not sure which one of the other 3 is most useful to me.

In the applied maths specialization I have to take the following subjects: real analysis, differential equations, vector calculus, probability, complex analysis, stochastic modeling, numerical computing with MATLAB and applied mathematical modelling. The only issue with this is that it has no discrete mathematics, which is useful for programming. I would love to replace MATLAB with discrete maths but I cannot.

In the discrete mathematics and operations research specialization I have to take the following subjects: real analysis, probability, discrete mathematics, techniques in operations research, complex analysis, graph theory and decision making. This problem with this is that there isn't much statistics.

In the statistics and stochastic processes specialization I have to take the following subjects: real analysis, probability, statistics, linear statistical models, stochastic modeling, probability and statistical inference and any other subject. The problem with this one is that it has little analysis and no discrete maths (unless I choose discrete maths as the optional subject, in which case these is little analysis).

What do you think? Which specialization will be most useful to me?
If I could choose any maths subjects I would choose: real analysis, group theory and linear algebra, probability, discrete maths, stochastic modeling, complex analysis, differential equations, vector calculus. Unfortunately I can't do this.

Would very much appreciate your advice.

 

[jasonRF]

My guess is that you will get the best advice by speaking with your academic advisor. They will know all the other classes you will take from the EE department (and others) and will be most familiar with the math options. For example, we don't know if you will already be learning enough differential equations (or probability, or vector calculus, or ...) in other classes even if you do not do a math major at all.

Also, it sounds like you want to take classes in everything! I understand, but you likely will not have time to take every class you would like to take, especially if you constrain yourself to taking what is required to satisfy a math major. Perhaps it makes more sense to simply take the math elective courses you want to take, even if it doesn't lead to a second major? Again, speak with your advisor, as they know you and your situation better than we ever will. I wish you the best,

jason

 

[member2357]

Thank you very much Jason.

The problem is that unless I do a second major, I have to pay for the extra subjects. If I do a second major, I get to do 4 subjects for free and cross credit the other 4 with my first major so maths is the only major I can cross credit with EECS.

I would love to be able to do only the subjects I am interested in from the maths department but I can't.

I only have to decide which maths specialisation I want to do a year from now because in first year and first half of second year, the subjects are the same for engineering and maths but I thought it would be a good idea to think about it now.

 

[heatengine516]

I am interested in machine learning and AI, finance, EE, CS etc.

Well, since you're an EECS major, that should cross EE and CS off the list!

I also believe that most engineering programs include enough economics type classes.

So for machine learning and AI, the discrete math specialization is going to be your best bet IMO.

 

[Khashishi]

Discrete maths is the most applicable to CS, because it's all digital. It's usually required to take some discrete maths for the CS degree. But applied maths can be very useful if you want to do simulations or modeling.

 

[verty]

The statistics concentration looks pretty nice to me. Statistical knowledge is something you could apply elsewhere, eg finance, and it should be relevant to AI.

A discrete math concentration I suppose would go very deeply into complexity theory, perhaps mathematical logic but also probability theory and advanced algorithms, MDCT, FFT, stuff like that. At least, I think knowing all the theory of these algorithms is probably the outcome you would want if you chose that specialization. But I am just surmising, this may be more applied math I suppose.

 

[caldweab]

I am an EECS major in first year of undergrad at university. I want to do a second major in mathematics but I have to choose a specialization. I am interested in machine learning and AI, finance, EE, CS etc.

The specializations my university offers are:

• pure mathematics
• applied mathematics.
• discrete mathematics and operations research
• statistics and stochastic processes

I have to chose only one of these specializations.

I am not interested in the pure maths specialization because I don't think it is useful to me. It involves a lot of real and complex analysis and abstract algebra.

I am not sure which one of the other 3 is most useful to me.

In the applied maths specialization I have to take the following subjects: real analysis, differential equations, vector calculus, probability, complex analysis, stochastic modeling, numerical computing with MATLAB and applied mathematical modelling. The only issue with this is that it has no discrete mathematics, which is useful for programming. I would love to replace MATLAB with discrete maths but I cannot.

In the discrete mathematics and operations research specialization I have to take the following subjects: real analysis, probability, discrete mathematics, techniques in operations research, complex analysis, graph theory and decision making. This problem with this is that there isn't much statistics.

In the statistics and stochastic processes specialization I have to take the following subjects: real analysis, probability, statistics, linear statistical models, stochastic modeling, probability and statistical inference and any other subject. The problem with this one is that it has little analysis and no discrete maths (unless I choose discrete maths as the optional subject, in which case these is little analysis).

What do you think? Which specialization will be most useful to me?
If I could choose any maths subjects I would choose: real analysis, group theory and linear algebra, probability, discrete maths, stochastic modeling, complex analysis, differential equations, vector calculus. Unfortunately I can't do this.

Would very much appreciate your advice.

I would say the applied math track. For one a lot of the courses are already required for your electrical engineering degree, like every engineer takes calculus 1-3, differential equations, and either linear algebra or an applied math course. Matlab is a very useful tool so don't write it off, I use Matlab and Maple a lot when doing work in upper level courses. It's better than doing tedious calculations by hand and you can generate nice looking reports. You're already going to have to take a statistics course too, for me it as statistical methods which is a 500 level course, some schools only require the 200 level statistics course. Why not use one of your elective courses to do a discrete math course?