Document:CMU-MSCF-essay2
| Li Quan Khoo |  li.khoo.11@ucl.ac.uk
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changtau2005@gmail.com |
 http://lqkhoo.com/wiki
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 https://github.com/lqkhoo
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 http://www.linkedin.com/pub/li-quan-khoo/89/a27/8aa
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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 neither studied them formally, or worked with them in any capacity at time of graduation.
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 for textbook RSA, some statistics, combinatorics, zero/first-order logic, and linear algebra. 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 and the properties of random arrival processes, specifically the Bernoulli and Poisson processes.
