These are the courses that I took from the University of Alabama at Birmingham’s Computer Science Department related to my Masters degree. Copied from the Graduate Catalog 2022-2023 on 2023-04-10 from http://catalog.uab.edu/graduate/collegeofartsciences/computerscience/#courseinventory
CS501 is a programming language overview course. The course will discuss computability, lexing, parsing, type systems, and ways to formalize a language’s semantics. The course will introduce students to major programming paradigms, such as functional programming and logic programming, and their realization in programming languages. Students will solve problems using different paradigms and study the impact on program design and implementation. The course enables students to assess strengths and weaknesses of different languages for problem solving.
Laboratory to accompany CS501.
Design and implementation of large-scale software systems, software development life cycle, software requirements and specifications, software design and implementation, verification and validation, project management and team-oriented software development.
Laboratory to accompany CS520.
Unix architecture and internals with an emphasis on Linux, shell scripting, distributions of Linux for various computing platforms including large and desktop computers, and embedded computing devices, introduction to the C programming language, systems programming in C covering signals and process control, networking, I/O, concurrency and synchronization, memory allocation, threads, debugging, library development and usage.
Laboratory to accompany CS532.
Introduction to web application design and development. Includes traditional web applications utilizing server-side scripting as well as client/server platforms. Covers responsive design for both mobile and desktop users, as well as hands on server provisioning and configuration. Other topics include web security problems and practices, authentication, database access, application deployment and Web API design, such as REpresentational State Transfer (REST).
Laboratory to accompany CS621.
Overview of parallel computing hardware, architectures, & programming paradigms; parallel programming using MPI, Pthreads, and OpenMP; design, development, and analysis of parallel algorithms for matrix computations, FFTs, and Sorting.
Introduction to cloud computing architectures and programming paradigms. Theoretical and practical aspects of cloud programming and problem-solving involving compute, storage and network virtualization. Design, development, analysis, and evaluation of solutions in cloud computing space including machine and container virtualization technologies.
Laboratory to accompany CS633.
The design and analysis of fundamental algorithms that underpin many fields of importance ranging from data science, business intelligence, finance and cyber security to bioinformatics. Algorithms to be covered include dynamic programming, greedy technique, linear programming, network flow, sequence matching, search and alignment, randomized algorithms, page ranking, data compression, and quantum algorithms. Both time and space complexity of the algorithms are analyzed.
This course provides a broad introduction to Natural Language Processing (Computational Linguistics) and its applications. Topics covered include language modeling with neural networks, sequence labelling algorithms (segmentation, chunking, tokenization, part-of-speech tagging and others ), syntactic and dependency parsing, vector-based representation models and using Deep Learning in NLP applications. Some application areas covered include information extraction and named entity recognition, semantic role labelling, word sense disambiguation, text generation, information retrieval, question answering, machine translation and other areas as time permits. There will be a focus on Deep Learning approaches using Tensorflow, PyTorch and keras for a major student project. Jupyter Notebooks will be used for assignments.
Deep Learning is a rapidly growing area of machine learning that has revolutionized speech recognition, image recognition and natural language processing. This course teaches you deep learning basics such as logistic regression, stochastic gradient descent, deep neural networks, convolutional neural networks and deep models for text and sequences. Students will also gain hands-on experience of using deep learning systems such as TensorFlow.
Fundamental concepts and techniques in statistical inference and big data analytics. Topics include high-dimensional space, singular value decomposition, random graphs, random walks and Markov chains, data streaming and sketching, and basics of data mining and machine learning.
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