Duluth campus

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Duluth Campus

Computer Science Minor

Computer Science
Swenson College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Department of Computer Science, 1114 Kirby Drive, 320 Heller Hall, Duluth, MN 55812 (218-726-7607; fax: 218-726-8240)
Email: cs@d.umn.edu
Website: http://www.d.umn.edu/cs/degrees/grad
  • Program Type: Graduate minor related to major
  • Requirements for this program are current for Fall 2022
  • Length of program in credits (master's): 8
  • Length of program in credits (doctoral): 12
  • This program does not require summer semesters for timely completion.
Computer science is a discipline that involves understanding the design of computers and computational processes. Study in the field ranges from the theoretical study of algorithms to the design and implementation of software at the systems and applications levels.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
Special Application Requirements:
Students interested in the minor are strongly encouraged to confer with their major field advisor and director of graduate studies, and the Computer Science director of graduate studies regarding feasibility and requirements.
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
The minimum cumulative GPA for minor field coursework is 3.00.
Minor Coursework (8 to 12 credits)
Master’s students select 8 credits, and doctoral students select 12 credits from the following in consultation with the Computer Science director of graduate studies:
CS 5112 - Advanced Theory of Computation (4.0 cr)
CS 5122 - Advanced Algorithms and Data Structures (4.0 cr)
CS 5212 - Computer Graphics (4.0 cr)
CS 5222 - Artificial Intelligence (4.0 cr)
CS 5232 - Introduction to Machine Learning and Data Mining (4.0 cr)
CS 5242 - Natural Language Processing (4.0 cr)
CS 5312 - Operating Systems (4.0 cr)
CS 5322 - Database Management Systems (4.0 cr)
CS 5332 - Computer Security (4.0 cr)
CS 5342 - Compiler Design (4.0 cr)
CS 5412 - Computer Architecture (4.0 cr)
CS 5422 - Computer Networks (4.0 cr)
CS 5432 - Sensors and Internet of Things (4.0 cr)
CS 5642 - Advanced Natural Language Processing (4.0 cr)
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Masters
Doctoral
  
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CS 5112 - Advanced Theory of Computation
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Mathematical theory of computation and complexity. Deterministic and nondeterministic Turing machines, Church-Turing Thesis, recursive and recursively enumerable languages. Lambda calculus. Undecidable problems, Rice's Theorem, undecidability of first-order logic and Gödels incompleteness theorem. Time and space complexity, reducibility, completeness for complexity classes, Cook's Theorem, P versus NP, Savitch's Theorem, complexity hierarchy. pre-req: Grad student, CS 3512 or 3531 or instructor consent
CS 5122 - Advanced Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Survey of advanced data structures and algorithms such as heaps and heapsort, quicksort, red-black trees, B-tress, hash tables, graph algorithms, divide and conquer algorithms, dynamic programming, and greedy algorithms. Methods for proving correctness and asymptotic analysis. pre-req: grad student; CS 2511, 2531 or 3512 or MATH 3355 or instructor consent; a grade of C- or better in all prerequisite courses
CS 5212 - Computer Graphics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduces the fundamentals of computer graphics to create 2D images from 3D data representations, the graphics pipeline, 3D representations of objects such as triangles and triangle meshes, surface material representations, color representation, vector and matrix mathematics, 3D coordinates and transformations, transport of light energy, global illumination, graphics rendering systemes, ray tracing, rasterization, real-time rendering, OpenGL and computer graphics hardware. prereq: graduate student, CS 2511, (2531 or 3512 or MATH 3355), (MATH 3280 or 3326) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5222 - Artificial Intelligence
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Principles and programming methods of artificial intelligence. Knowledge representation methods, state space search strategies, and use of logic for problem solving. Applications chosen from among expert systems, planning, natural language understanding, uncertainty reasoning, machine learning, and robotics. Lectures and labs will utilize suitable high-level languages (e.g., Python or Lisp). prereq: grad student, 2511, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5232 - Introduction to Machine Learning and Data Mining
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduction to primary approaches to machine learning and data mining. Methods selected from decision trees, neural networks, statistical learning, genetic algorithms, support vector machines, ensemble methods, and reinforcement learning. Theoretical concepts associated with learning, such as inductive bias and Occam's razor. This is a potential Master's project course. prereq: grad student, 2511, 2531 or 3512 or MATH 3355, Stat 3611 or 3411, Math 3280 or 3326 or instructor consent; a grade of C- or better is required in all prerequisite courses
CS 5242 - Natural Language Processing
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Techniques for creating computer programs that analyze, generate, and understand written human language. Emphasizes broad coverage of both rule-based and empirical data-driven methods. Topics include word-level approaches, syntactic analysis, and semantic interpretation. Applications selected from conversational agents, sentiment analysis, information extraction, and question answering. Significant research project that includes experimental results, written report, and clear grasp of ethical considerations involved. prereq: CS 2511, (2531 or 3512 or MATH 3355), grad student or instructor consent; a grade of C- or better is required in the prerequisite course; credit will not be granted if already received for CS 4242 or 5761
CS 5312 - Operating Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Operating system as resource manager. Modern solutions to issues such as processor management and scheduling, concurrency and related problems including deadlocks, memory management and protection, file system design, virtualization, distributed and cloud computing. Concepts including concurrency are illustrated via laboratory assignments, This is a potential Master's project course. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5322 - Database Management Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Entropy and the underlying characteristics of text. Encryption-basic techniques based on confusion and diffusion and modern day encryption. Access, information flow and inference control. Program threats and intrusion detection/prevention. Network and Internet security. Firewalls, trusted systems, network authentication. Privacy and related social issues. Planning, Incidents, and Recovery. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent; a grade of C- or better is required in all prerequisite courses
CS 5332 - Computer Security
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Entropy and the underlying characteristics of text. Encryption-basic techniques based on confusion and diffusion and modern day encryption. Access, information flow and inference control. Program threats and intrusion detection/prevention. Network and Internet security. Firewalls, trusted systems, network authentication. Privacy and related social issues. Planning, Incidents, and Recovery. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent; a grade of C- or better is required in all prerequisite courses
CS 5342 - Compiler Design
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
A selection from the following topics: finite-state grammars, lexical analysis, and implementation of symbol tables. Context-free languages and parsing techniques. Syntax-directed translation. Run-time storage allocation. Intermediate languages. Code generation methods. Local and global optimization techniques. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5412 - Computer Architecture
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Broad coverage of computer architecture, with a focus on the development of the stored program computer and the historical evolution of architectures. Includes coverage of significant architectures based on vacuum tubes, transistors, and integrated circuits. Impact of Moore?s Law and possible paradigms for the future including quantum and molecular computing. prereq: 2521, (2531 or 3512 or MATH 3355), grad student or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5422 - Computer Networks
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduction to computer networking, network programming, networking hardware and associated network protocols. Layered network architecture, network services, and implementation of computer networking software. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5432 - Sensors and Internet of Things
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course will introduce a broad range of sensors such as wearable biosensors that measure physiological changes, psychological changes, brain electrical activity, muscle impedance, and other sensors such as kinematic sensors, virtual reality, motion capture, luminosity and a range of robots, varying in size, features and autonomous capabilities, while emphasizing the basic principles of sensing for temperature, motion, sound, light, position, displacement, etc. IoT are ubiquitous systems that are built using embedded processors, sensors, other electronics and communication mechanisms. You will be introduced to IoTs through lectures, hands-on labs, and research papers. You will interface (embedded programming) various sensors with an AI based System-on-a-Chip (SoC) to learn to design a complete IoT system. In addition, you will learn to identify and mitigate any ethical issues related to this topic. Students will also learn the latest advances in the field of sensors and IoTs. pre-req: grad student, CS 2511, CS 2521, (CS 2531 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses, no credit if CS 4432 taken
CS 5642 - Advanced Natural Language Processing
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Advanced techniques for creating computer programs that analyze, generage, and understand written human language. Emphasizes current empirical data-driven methods. Topics include sentence level representations, vector semantics, and models of document understanding. Applications selected from word sense discovery, machine translation, sentiment and option mining, and social computing. Significant research project that includes experimental results, written report, and clear grasp of ethical considerations involved. pre-req: CS 4242 or 5242, grad student or instructor consent; a grade of C- or better is required in the prerequisite course.