Difference between revisions of "Document:CMU-MSCF-essay2"

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I have supplemented this area with lectures and coursework from multiple units offered by MIT OpenCourseware, including single/multivariable calculus, differential equations, linear algebra, and probabilistic systems analysis and applied probability, as I found them to be very useful in understanding some of the machine learning literature. Most of my studies on undergraduate-level probability was in preparation for the MSCF programme, and I have covered both discrete and continuous probability density functions (marginal, joint, derived distributions), the properties of random arrival processes, specifically the Bernoulli and Poisson processes, and Markov chains.
 
I have supplemented this area with lectures and coursework from multiple units offered by MIT OpenCourseware, including single/multivariable calculus, differential equations, linear algebra, and probabilistic systems analysis and applied probability, as I found them to be very useful in understanding some of the machine learning literature. Most of my studies on undergraduate-level probability was in preparation for the MSCF programme, and I have covered both discrete and continuous probability density functions (marginal, joint, derived distributions), the properties of random arrival processes, specifically the Bernoulli and Poisson processes, and Markov chains.
  
I have not had any formal financial training, but my understanding is roughly at the level of introductory textbooks to the subject, such as Principles of Corporate Finance (Richard A. Brealy et al., McGraw-Hill), and The Basics of Finance (Frank J. Fabozzi, Wiley), most definitely about fundamental concepts like stocks, bonds, calls, puts, long and short positions, market spread, depth, (continuous) interest rates, present value, etc. It was enough to participate in events and a hackathon run by J.P. Morgan, although they were recruiting for software engineers. I have not covered more advanced topics, like modelling financial time series with random processes, and models built on these assumptions, like the Black-Scholes model, but those are what the MSCF programme is about.
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I have not had any formal financial training, but my understanding is roughly at the level of introductory textbooks to the subject, such as Principles of Corporate Finance (Richard A. Brealy et al., McGraw-Hill), and The Basics of Finance (Frank J. Fabozzi, Wiley), and I most definitely understand fundamental concepts like stocks, bonds, calls, puts, long and short positions, market spread, depth, (continuous) interest rates, present value, etc. It was enough to participate in events and a hackathon run by J.P. Morgan, although they were recruiting for software engineers. I have not covered more advanced topics, like modelling financial time series with random processes, and models built on these assumptions, like the Black-Scholes model, but learning about those is what the MSCF programme is about.
  
  

Revision as of 23:25, 8 January 2016


My degree prior to applying for the MSCF programme is an MEng in Computer Science. That covered the core components of any CompSci degree, including object-oriented and functional programming, databases, algorithms, complexity, etc. The full list of subjects is in the transcript included with my application. I have mostly programmed in Python, C#, and Java in production and research code, although I have used C and C++ in some capacity, but mostly in security, OS, and networking coursework. My focus during the 3rd and 4th years of the degree was on machine learning, data mining, and to some degree, natural language processing, so I am familiar with frameworks like scikit-learn and NLTK for Python, CMU's own TweetNLP, and datasets related to those areas. I have experience with regression classification methods, support vector machines, and variants, like logistic regression, regularization, or with kernels, dimensional reduction methods like PCA, and unsupervised learning methods, like k-means clustering. I have some understanding about neural networks, and probabilistic graphical models, such as Bayes nets and Markov models, but I have not formally studied them.

The degree was not mathematics-heavy; we had two units of applied mathematics, which were mostly a mix of topics required to work with computer science, like basic number theory enough for textbook RSA, some statistics, combinatorics, zero/first-order logic, and linear algebra. Here is a more detailed list pulled directly from the syllabus: Sets theoretic notation. Relations, in particular equivalence relations. Injections, surjections, bijections and their inverses. Cardinality of sets. The symmetry group, disjoint cycle notation; the sign of a permutation. Abstract groups and Lagrange’s Theorem. Euclid’s algorithm, solving linear congruences, Fermat’s little theorem, the Euler totient function, application to public key cryptography. Linear algebra. The correspondence between linear maps and matrices. Associativity and non-commutativity of matrix manipulation, Gaussian elimination. LU decomposition. Inverting matrices. Determinants. Eigenvalues and eigenvectors. Diagonalizing matrices and calculating polynomials in diagonalizing matrices. Singular value decomposition.

I have supplemented this area with lectures and coursework from multiple units offered by MIT OpenCourseware, including single/multivariable calculus, differential equations, linear algebra, and probabilistic systems analysis and applied probability, as I found them to be very useful in understanding some of the machine learning literature. Most of my studies on undergraduate-level probability was in preparation for the MSCF programme, and I have covered both discrete and continuous probability density functions (marginal, joint, derived distributions), the properties of random arrival processes, specifically the Bernoulli and Poisson processes, and Markov chains.

I have not had any formal financial training, but my understanding is roughly at the level of introductory textbooks to the subject, such as Principles of Corporate Finance (Richard A. Brealy et al., McGraw-Hill), and The Basics of Finance (Frank J. Fabozzi, Wiley), and I most definitely understand fundamental concepts like stocks, bonds, calls, puts, long and short positions, market spread, depth, (continuous) interest rates, present value, etc. It was enough to participate in events and a hackathon run by J.P. Morgan, although they were recruiting for software engineers. I have not covered more advanced topics, like modelling financial time series with random processes, and models built on these assumptions, like the Black-Scholes model, but learning about those is what the MSCF programme is about.