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3 typo corrections
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Well, you were in a computer science department and not in a computer engineering department. It would be a reasonable expectingexpectation that you would understand the fundamental mechanics of "computer stuff" and possibly continue your studies in research. I am not a computer scientist so examples may be limited here.

  • cryptography: Beyond understanding RSA schemes there are many interesting research areas and applications. For example elliptic curve cryptography uses serious level of mathematics or homomorphic encryption uses encryption schemes imitating a mathematical concept of morphism to process encrypted data without decryption (for example ordering numbers)
  • communications theory: Coding theory is heavily mathematical. It has some subfields that require more than a basic understanding of linear algebra. I have heard that research in error correcting codes is quite non-trivial. Their main property is to detect and correct possible errors in communication. This is, likely, extra helpful for applications where communications are more likely to have errors. Consider space, polar or deep ocean exploration. On the theoretical side of coding theory, famously, someone used algebraic geometry to show a theoretical upper bound for an invariant (don't remember its name) was as best as we could hope. (This would mean there are codes that give this exact value for the invariant as the upper bound predicts)
  • Image recognition and machine learning also uses serious levels of linear algebra. Serious in the sense that the intuition of 3 dimensional vector spaces and ability to multiply some matrices would not be sufficient as you would be comparing vector spaces of big number dimensions. I am sure a more literate person would be more helpful on this point.
  • Haskell is a programming language based on a language mathematicians call category theory. I do not know benefits of using this Haskell but some people seem to love it. I would say however that category theory is very nontrivial. I would say an average student after completing an undergraduate degree in mathematics would have a very basic understanding of it. It is highly conceptual and its origins and most of its examples are usually graduate school material. Hence it would be really helpful to have a general mathematics background in order to relate what is going on.

Well, you were in a computer science department and not in a computer engineering department. It would be a reasonable expecting that you would understand the fundamental mechanics of "computer stuff" and possibly continue your studies in research. I am not a computer scientist so examples may be limited here.

  • cryptography: Beyond understanding RSA schemes there are many interesting research areas and applications. For example elliptic curve cryptography uses serious level of mathematics or homomorphic encryption uses encryption schemes imitating a mathematical concept of morphism to process encrypted data without decryption (for example ordering numbers)
  • communications theory: Coding theory is heavily mathematical. It has some subfields that require more than a basic understanding of linear algebra. I have heard that research in error correcting codes is quite non-trivial. Their main property is to detect and correct possible errors in communication. This is, likely, extra helpful for applications where communications are more likely to have errors. Consider space, polar or deep ocean exploration. On the theoretical side of coding theory, famously, someone used algebraic geometry to show a theoretical upper bound for an invariant (don't remember its name) was as best as we could hope. (This would mean there are codes that give this exact value for the invariant as the upper bound predicts)
  • Image recognition and machine learning also uses serious levels of linear algebra. Serious in the sense that the intuition of 3 dimensional vector spaces and ability to multiply some matrices would not be sufficient as you would be comparing vector spaces of big number dimensions. I am sure a more literate person would be more helpful on this point.
  • Haskell is a programming language based on a language mathematicians call category theory. I do not know benefits of using this Haskell but some people seem to love it. I would say however that category theory is very nontrivial. I would say an average student after completing an undergraduate degree in mathematics would have a very basic understanding of it. It is highly conceptual and its origins and most of its examples are usually graduate school material. Hence it would be really helpful to have a general mathematics background in order to relate what is going on.

Well, you were in a computer science department and not in a computer engineering department. It would be a reasonable expectation that you would understand the fundamental mechanics of "computer stuff" and possibly continue your studies in research. I am not a computer scientist so examples may be limited here.

  • cryptography: Beyond understanding RSA schemes there are many interesting research areas and applications. For example elliptic curve cryptography uses serious level of mathematics or homomorphic encryption uses encryption schemes imitating a mathematical concept of morphism to process encrypted data without decryption (for example ordering numbers)
  • communications theory: Coding theory is heavily mathematical. It has some subfields that require more than a basic understanding of linear algebra. I have heard that research in error correcting codes is quite non-trivial. Their main property is to detect and correct possible errors in communication. This is, likely, extra helpful for applications where communications are more likely to have errors. Consider space, polar or deep ocean exploration. On the theoretical side of coding theory, famously, someone used algebraic geometry to show a theoretical upper bound for an invariant (don't remember its name) was as best as we could hope. (This would mean there are codes that give this exact value for the invariant as the upper bound predicts)
  • Image recognition and machine learning also uses serious levels of linear algebra. Serious in the sense that the intuition of 3 dimensional vector spaces and ability to multiply some matrices would not be sufficient as you would be comparing vector spaces of big number dimensions. I am sure a more literate person would be more helpful on this point.
  • Haskell is a programming language based on a language mathematicians call category theory. I do not know benefits of using this Haskell but some people seem to love it. I would say however that category theory is very nontrivial. I would say an average student after completing an undergraduate degree in mathematics would have a very basic understanding of it. It is highly conceptual and its origins and most of its examples are usually graduate school material. Hence it would be really helpful to have a general mathematics background in order to relate what is going on.
2 typo corrections
source | link

Well, you were in a computer science department and not in a computer engineering department. It would be a reasonable expection fromexpecting that you towould understand the fundamental mechanics of "computer stuff" and possibly continue your studies in research. I am not a computer scientist so examples may be limited here.

  • cryptography: Beyond understanding RSA shemesschemes there are many interesting research areas and applications. For example elipticelliptic curve cryptography uses serious level of mathematics or homomorphic encryption uses encryption schemes immitatingimitating a mathematical concept of morphism to process encrypted data without decryption (for example ordering numbers)
  • communications theory: Coding theory is heavily mathematical. It has some subfields that require more than a basic understanding of linear algebra. I have heard that research in error correcting codes is quite non-trivial. Their main property is to detect and correct possible errors in communication. This is, likely, extra helpful for applications where communications are more likely to have errors. Consider space, polar or deep ocean exploration. On the theoretical side of coding theory, famously, someone used algebraic geometry to show a teoreticaltheoretical upper bound for an invariant (don't remember its name) was as best as we could hope. (This would mean there are codes that give this exact value for the invariant as the upper bound predicts)
  • Image recognition and machine learning also uses serious levels of linear algebra. Serious in the sense that the intuition of 3 dimensional vector spaces and ability to multiply some matrices would not be sufficentsufficient as you would be comparing vector spaces of big number dimensions. I am sure a more literate person would be more helpful on this point.
  • Haskell is a programming language based on a language mathematicians call category theory. I do not know benefits of using this haskellHaskell but some people seem to love it. I would say however that category theory is very nontrivial. I would say approximatean average student after completing an undergraduate degree in mathematics would have a very basic understanding of it. It is highly conceptual and its origins and most of its examples are usually graduate school material. Hence it would be really helpful to have a general mathematics background in order to relate what is going on.

Well, you were in a computer science department and not in a computer engineering department. It would be a reasonable expection from you to understand the fundamental mechanics of "computer stuff" and possibly continue your studies in research. I am not a computer scientist so examples may be limited here.

  • cryptography: Beyond understanding RSA shemes there are many interesting research areas and applications. For example eliptic curve cryptography uses serious level of mathematics or homomorphic encryption uses encryption schemes immitating a mathematical concept of morphism to process encrypted data without decryption (for example ordering numbers)
  • communications theory: Coding theory is heavily mathematical. It has some subfields that require more than a basic understanding of linear algebra. I have heard that research in error correcting codes is quite non-trivial. Their main property is to detect and correct possible errors in communication. This is, likely, extra helpful for applications where communications are more likely to have errors. Consider space, polar or deep ocean exploration. On the theoretical side of coding theory, famously, someone used algebraic geometry to show a teoretical upper bound for an invariant (don't remember its name) was as best as we could hope. (This would mean there are codes that give this exact value for the invariant as the upper bound predicts)
  • Image recognition and machine learning also uses serious levels of linear algebra. Serious in the sense that the intuition of 3 dimensional vector spaces and ability to multiply some matrices would not be sufficent as you would be comparing vector spaces of big number dimensions. I am sure a more literate person would be more helpful on this point.
  • Haskell is a programming language based on a language mathematicians call category theory. I do not know benefits of using this haskell but some people seem to love it. I would say however that category theory is very nontrivial. I would say approximate student after completing an undergraduate degree in mathematics would have a very basic understanding of it. It is highly conceptual and its origins and most of its examples are usually graduate school material. Hence it would be really helpful to have a general mathematics background in order to relate what is going on.

Well, you were in a computer science department and not in a computer engineering department. It would be a reasonable expecting that you would understand the fundamental mechanics of "computer stuff" and possibly continue your studies in research. I am not a computer scientist so examples may be limited here.

  • cryptography: Beyond understanding RSA schemes there are many interesting research areas and applications. For example elliptic curve cryptography uses serious level of mathematics or homomorphic encryption uses encryption schemes imitating a mathematical concept of morphism to process encrypted data without decryption (for example ordering numbers)
  • communications theory: Coding theory is heavily mathematical. It has some subfields that require more than a basic understanding of linear algebra. I have heard that research in error correcting codes is quite non-trivial. Their main property is to detect and correct possible errors in communication. This is, likely, extra helpful for applications where communications are more likely to have errors. Consider space, polar or deep ocean exploration. On the theoretical side of coding theory, famously, someone used algebraic geometry to show a theoretical upper bound for an invariant (don't remember its name) was as best as we could hope. (This would mean there are codes that give this exact value for the invariant as the upper bound predicts)
  • Image recognition and machine learning also uses serious levels of linear algebra. Serious in the sense that the intuition of 3 dimensional vector spaces and ability to multiply some matrices would not be sufficient as you would be comparing vector spaces of big number dimensions. I am sure a more literate person would be more helpful on this point.
  • Haskell is a programming language based on a language mathematicians call category theory. I do not know benefits of using this Haskell but some people seem to love it. I would say however that category theory is very nontrivial. I would say an average student after completing an undergraduate degree in mathematics would have a very basic understanding of it. It is highly conceptual and its origins and most of its examples are usually graduate school material. Hence it would be really helpful to have a general mathematics background in order to relate what is going on.
1
source | link

Well, you were in a computer science department and not in a computer engineering department. It would be a reasonable expection from you to understand the fundamental mechanics of "computer stuff" and possibly continue your studies in research. I am not a computer scientist so examples may be limited here.

  • cryptography: Beyond understanding RSA shemes there are many interesting research areas and applications. For example eliptic curve cryptography uses serious level of mathematics or homomorphic encryption uses encryption schemes immitating a mathematical concept of morphism to process encrypted data without decryption (for example ordering numbers)
  • communications theory: Coding theory is heavily mathematical. It has some subfields that require more than a basic understanding of linear algebra. I have heard that research in error correcting codes is quite non-trivial. Their main property is to detect and correct possible errors in communication. This is, likely, extra helpful for applications where communications are more likely to have errors. Consider space, polar or deep ocean exploration. On the theoretical side of coding theory, famously, someone used algebraic geometry to show a teoretical upper bound for an invariant (don't remember its name) was as best as we could hope. (This would mean there are codes that give this exact value for the invariant as the upper bound predicts)
  • Image recognition and machine learning also uses serious levels of linear algebra. Serious in the sense that the intuition of 3 dimensional vector spaces and ability to multiply some matrices would not be sufficent as you would be comparing vector spaces of big number dimensions. I am sure a more literate person would be more helpful on this point.
  • Haskell is a programming language based on a language mathematicians call category theory. I do not know benefits of using this haskell but some people seem to love it. I would say however that category theory is very nontrivial. I would say approximate student after completing an undergraduate degree in mathematics would have a very basic understanding of it. It is highly conceptual and its origins and most of its examples are usually graduate school material. Hence it would be really helpful to have a general mathematics background in order to relate what is going on.