MS PLAN II: Comprehensive Exam, Standard Option (Effective Fall 2015)

 

Computer Science or Computer Engineering

40 units

 

BREADTH (12 units)

  • Computer Science majors must take one course from each of the three  breadth areas: Theory, Systems, and Applications.
  • Computer Engineering majors must take two courses from the Systems area AND one course from either Theory or Applications.
  • Courses must be taken for a letter grade and completed with a grade of B- or higher.
Theory
  • CSE 200 - Computability Complexity
  • CSE 201A - Advanced Complexity
  • CSE 202 - Algorithm Design and Analysis
  • CSE 203A - Advanced Algorithms
  • CSE 205A - Logic in Computer Science
  • CSE 207 - Modern Cryptography
Systems
  • CSE 221 - Operating Systems
  • CSE 222A - Computer Communication Networks
  • CSE 223B - Distributed Computing and Systems
  • CSE 231 - Advanced Compiler Design
  • CSE 237A - Embedded Systems
  • CSE 237B - Embedded Software
  • CSE 237C - Validation/Testing of Embedded Systems
  • CSE 237D - Embedded Systems Design
  • CSE 240A - Principles of Computer Architecture
  • CSE 241A - VLSI Integration of Computing Circuitry
  • CSE 243A - VLSI CAD
  • CSE 244A - VLSI Test
  • CSE 291 - Graduate Networked Systems
Applications
  • CSE 210 - Principles of Software Engineering
  • CSE 216 - Human-Computer Interaction
  • CSE 230 - Principles of Programming Languages
  • CSE 232 - Principles of Database Systems
  • CSE 250A - AI: Probabilistic Reasoning and Learning
  • CSE 250B - AI: Learning Algorithms
  • CSE 252A - Computer Vision I
  • CSE 252B - Computer Vision II
  • CSE 260 - Parallel Computation
  • CSE 280A - Algorithms in Computational Biology

 

DEPTH (12 units)

  • Computer Science majors must take three courses (12 units) from one depth area on this list.
  • Computer Engineering majors must take three courses (12 units) from the Computer Engineering depth area only.
  • Courses must be taken for a letter grade.

 

Artificial Intelligence

  • CSE 250A - AI: Probabilistic Reasoning and Learning
  • CSE 250B - AI: Learning Algorithms
  • CSE 250C - Machine Learning Theory
  • CSE 253 - Neural Networks/Pattern Recognition
  • CSE 254 - Statistical Learning
  • CSE 255 - Data Mining and Predictive Analysis
  • CSE 256 - Statistical Natural Language Processing
  • CSE 258 - Recommended Systems and Web Mining
  • CSE 258A - Cognitive Modeling
  • CSE 291 - Statistical Learning and Combinatorics
  • CSE 291 - Graph Mining/Network Analysis
  • CSE 291 - Latent Variable Models
  • CSE 291 - Topics in Statistical NLP
  • CSE 291 - Statistical Learning Theory
  • CSE 291 - Automated Reasoning in AI
  • CSE 291: Recommender Systems
  • CSE 291: Adv. Analytics and ML Systems
  • CSE 291: Convex Optimization
  • CSE 291: Machine Learning on 3D Data
  • CSE 291: Advanced Statistical NLP
  • COGS 243 - Statistical Data Analysis
  • COGS 260 - Image Recognition (w/ Z. Tu)
  • MAE 242 - Robot Motion Planning
  • ECE 276C -Robot Reinforcement Learning

Computer Engineering

  • CSE 231 - Advanced Compiler Design
  • CSE 237A - Introduction to Embedded Computing
  • CSE 237B - Software for Embedded Systems
  • CSE 237C - Validation and Testing of Embedded Systems
  • CSE 237D - Design Automation and Prototyping for Embedded Systems
  • CSE 240A - Principles of Computer Architecture
  • CSE 240B - Parallel Computer Architecture
  • CSE 240C - Advanced Microarchitecture
  • CSE 241A - VLSI Integration of Computing Circuitry
  • CSE 243A - Introduction to Synthesis Methodologies in VLSI CAD
  • CSE 244A - VLSI Test
  • CSE 245 - Computer Aided Circuit Simulation and Verification  
  • CSE 248 - Algorithmic and Optimization Foundations for VLSI CAD
  • ECE 260A - VLSI Digital System Algorithms & Architectures
  • ECE 260B - VLSI Integrated Circuits & Systems Design
  • ECE 260C - VLSI Advanced Topics
  • ECE 284 - Special Topics in Computer Engineering 

Computer Systems

  • CSE 221 - Operating Systems
  • CSE 222A - Computer Communication Networks
  • CSE 222B - Internet Algorithmics
  • CSE 223A - Principles of Distributed Computing
  • CSE 223B - Distributed Computing and Systems
  • CSE 227 - Computer Security 
  • CSE 260 - Parallel Computation 
  • CSE 262 - System Support for Applications of Parallel Computation 
  • CSE 291 - Distributed Systems
  • CSE 291 - Cloud Computing
  • CSE 291 - Storage Systems
  • CSE 291 - Adv. Analytics & ML Systems
  • CSE 291: Language Based Security
  • CSE 291: Graduate Networked Systems

Database Systems

  • CSE 232 - Principles of Database Systems
  • CSE 232B - Database System Implementation 
  • CSE 233 - Database Theory
  • (Third course by petition)
  • CSE 291 - Advanced Analytics
  • CSE 291 - MGMT Large-Scale Graph Data
  • CSE 291: Adv. Analytics and ML Systems

Graphics and Vision

  • CSE 252A - Computer Vision I
  • CSE 252B - Computer Vision II
  • CSE 252C - Selected Topics in Vision and Learning
  • CSE 272 - Advanced Image Synthesis
  • CSE 274 - Selected Topics in Graphics
  • COGS 260 - Image Recognition
  • CSE 291 - Pattern Recognition

Human-Computer Interaction

  • CSE 170 - Interaction Design
  • CSE 216 - Research Topics in HCI
  • CSE 218 - Advanced Topucs in Software Engineering
  • CSE 250A - AI: Probabilistic Reasoning and Learning
  • COGS 220 - Information Visualization 
  • COGS 231- Human Centered Programming
  • COGS 260 - Crowdsourcing Research
  • CSE 219 - 1 unit seminar (recommended but does NOT fulfill the depth requirement)

Programming Languages, Compilers, and Software Engineering

  • CSE 210 - Principles of Software Engineering
  • CSE 218 - Advanced Topics in Software Engineering 
  • CSE 230 - Principles of Programming Languages
  • CSE 231 - Advanced Compiler Design

Bioinformatics

  • CSE 280A - Algorithms in Computational Biology
  • CSE 282 - Bioinformatics II: Sequence and Structure Analysis - Methods and Applications
  • CSE 283 - Bioinformatics III: Functional Genomics 
  • MATH 283 - Statistical Methods in Bioinformatics

Theoretical Computer Science

  • CSE 200 - Computability and Complexity
  • CSE 201A - Advanced Complexity
  • CSE 202 - Algorithm Design and Analysis
  • CSE 203A - Advanced Algorithms
  • CSE 205 - Logic in Computer Science
  • CSE 206A - Lattice Algorithms and Applications
  • CSE 207 - Modern Cryptography 
  • CSE 208 - Advanced Cryptography 

Robotics

Mandatory:

  • CSE 276A Introduction to Robotics

Choose One or Two Courses:

  • CSE 276B Human Robot Interaction
  • CSE 276C Mathematics for Robotics
  • CSE 276D Healthcare Robotics 

Choose Zero or One Courses:

  • CSE 250B Artificial Intelligence
  • CSE 252B Computer Vision

ELECTIVES AND RESEARCH (16 Units)

Electives are chosen from graduate courses in CSE, ECE and Mathematics, or from other departments as approved, per the ELECTIVES EXCEPTION LISTElective courses must be completed for letter grade. 

Maximum of ONE undergraduate CSE upper-division course from approved ELECTIVES EXCEPTION LIST  is permitted towards Electives. **Note, students are not permitted to double-count the graduate version or a similar course of the undergraduate exception towards the degree requirements. In addition, seats are not guaranteed for approved list of CSE undergraduate course. Undergraduate students receive priority seating. Please direct questions regarding exception list to cse-ms-advisors@eng.ucsd.edu

These requirements are the same for both Computer Science and Computer Engineering majors.Students electing Plan II may choose to pursue a research project with an adviser while enrolled in four units of research, normally CSE 293. A maximum of four units of research may be applied to the Electives and Research requirement.

Courses must be completed for a letter grade, except research units that are taken on a Satisfactory/Unsatisfactory basis.  Seminar and teaching units may not count toward the Electives and Research requirement, although both are encouraged.  

CAPSTONE: Comprehensive Exam  

Under this plan, the student must pass the comprehensive examination designed to test the student’s knowledge in fundamental computer science material. The examination covers a wide range of material across the breadth areas, enabling students to demonstrate the cumulative knowledge they have gained. 

Comprehensive Guidelines Effective 2016       Comp Exam Guidelines