MS Plan II- Comprehensive Exam, Interdisciplinary Option (Effective Fall 2015)

M.S. Plan I - Thesis 

M.S. Plan II- Comprehensive Exam, Standard Option 


Computer Science or Computer Engineering (48 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 Systems Applications
  • CSE 200 - Computability Complexity
  • CSE 201A - Advanced Complexity
  • CSE 202 - Algorithm Design and Analysis
  • CSE 203A - Advanced Algorithms
  • CSE 203B/291 - Convex Optimization (*students that completed ECE273 or Math 245B will not be eligible to enroll or count the course)
  • CSE 205A - Logic in Computer Science
  • CSE 207 - Modern Cryptography
  • CSE 221 - Operating Systems
  • CSE 222A - Computer Communication Networks
  • CSE 223B - Distributed Computing and Systems
  • CSE 224 (formerly CSE 291) - Graduate Networked 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 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

 

PRIMARY 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 203B /CSE 291 - Convex Optimization (*students that completed ECE273 or Math 245B will not be eligible to enroll in CSE 203B or count the course)
  • 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 - Recommender Systems and Web Mining
  • CSE 258A - Cognitive Modeling
  • CSE 252C - Advanced Computer Vision (Prof. Manmohan Chandraker's Section)
  • CSE 291 - 3D-Centric Machine Learning
  • CSE 291-  Advances in 3D Reconstruction
  • CSE 291 - Advanced Deep Learning (*Cottrell Section Only)
  • CSE 291- Adv. Analytics and ML Systems
  • CSE 291- Advanced Statistical NLP
  • CSE 291 - Advanced Data-Driven Text Mining
  • CSE 291 - Algorithms for Big Data
  • CSE 291 - Automated Reasoning in AI
  • CSE 291: Data Systems for Machine Learning
  • CSE 291-  Deep Learning for Sequences
  • CSE 291 - Graph Mining/Network Analysis
  • CSE 291 - Latent Variable Models
  • CSE 291- Machine Learning on 3D Data
  • CSE 291- Machine Learning on Geometrical Data
  • CSE 291 - Probabilistic Approaches to Unsupervised Learning 
  • CSE 291-  Recommender Systems
  • CSE 291- Search and Optimization
  • CSE 291 - Statistical Learning and Combinatorics
  • CSE 291 - Statistical Learning Theory
  • CSE 291 - Topics in Statistical NLP
  • CSE 291 - Trustworthy Machine Learning (Chaudhuri)
  • CSE 291- Unsupervised Learning
  • COGS 243 - Statistical Data Analysis
  • COGS 225 (Formerly COGS260) - Image Recognition (w/ Z. Tu)
  • DSC 291- Data Science: Scientists/Engineers (Yoav Freund)
  • ECE 273 - Convex Optimization and Applications
  • ECE 276C -Robot Reinforcement Learning
  • ECE 285 - Intel Vehicles/Asst Systems
  • ECE 285 - Spec Topic/Signal&Imag/Robotic 
    Machine Learning/Image Process
  • MAE 242 - Robot Motion Planning
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 240D - Application Specific Processors
  • 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
  • CSE 260 - Parallel Computation 
  • CSE 291 - Memory/storage technologies and applications
  • CSE 291 - Topics in Embedded Computing and Communication
  • 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 224 (formerly CSE 291) - Graduate Networked Systems
  • CSE 227 - Computer Security 
  • CSE 260 - Parallel Computation 
  • CSE 262 - System Support for Applications of Parallel Computation 
  • CSE 291 - Adv. Analytics & ML Systems
  • CSE 291 - Applied Cryptography 
  • CSE 291 - Cloud Computing
  • CSE 291 - Data Systems for Machine Learning
  • CSE 291 - Distributed Systems
  • CSE 291-  Language Based Security
  • CSE 291 - Memory/storage technologies and applications
  • CSE 291 - Storage Systems
  • CSE 291 - Topics in Embedded Computing and Communication
  • CSE 291 - Virtualization
Database Systems
  • CSE 232 - Principles of Database Systems
  • CSE 232B - Database System Implementation 
  • CSE 233 - Database Theory
  • CSE 291 - Advanced Analytics
  • CSE 291 - MGMT Large-Scale Graph Data
  • CSE 291: Adv. Analytics and ML Systems
  • CSE 291: Advanced Topic: Data Models in Big Data Era 
  • CSE 291: Data Systems for Machine Learning
Graphics and Vision
  • CSE 163 - Advanced Comp Graphics
  • CSE 168 -Cmp Graphics II Rendering
  • 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
  • CSE 291 - 3D-Centric Machine Learning
  • CSE 291: Advances in 3D Reconstruction
  • CSE 291 - Computational Photography
  • CSE 291: Deep Learning for Sequences
  • CSE 291 - Pattern Recognition
  • CSE 291: Physical Simulation 
  • CSE 291: Recent Advances in Computer Vision
  • COGS 260 - Image 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
  • CSE 291 - HCI for Health
  • CSE 291 - Computer Science Education Research
  • 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
  • CSE 291- Program Synthesis
Bioinformatics
  • CSE 280A - Algorithms in Computational Biology
  • CSE 282 - Bioinformatics II: Sequence and Structure Analysis - Methods and Applications
  • CSE 283 - Bioinformatics III: Functional Genomics 
  • CSE 284 - Personal 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 
  • CSE 291 - Communication Complexity
  • CSE 291 - Topics in 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

 

SECONDARY DEPTH (12 Units)

Twelve units in one of the approved areas outside of CSE. Courses must be taken for a letter grade. The 12 units of the secondary depth must be taken from only one area and approved by the MS committee: the Departments of Cognitive Science, Electrical and Computer Engineering, Mechanical and Aerospace Engineering, Structural Engineering or the JSOE Management Courses.

ELECTIVES AND RESEARCH (12 Units) 

Electives are chosen from graduate courses in CSE, ECE and Mathematics or from other departments as approved, per the approved  ELECTIVES EXCEPTION LIST.

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.  

Graduate/Undergraduate Course Restrictions

  • MS Students who completed one of the following six undergraduate versions of the course at UCSD are not allowed to enroll or count the graduate version of the course. For example, if a student completes CSE 130 at UCSD at UCSD, they may not take CSE 230 for credit toward their MS degree.
  • MS students may not attempt to take both the undergraduate and graduate version of these six courses for degree credit.  In order words, only one of these two courses may count toward the MS degree (if eligible under current breadth, depth, or electives).

CSE-118/CSE-218 (Instructor Dependent/ If completed by same instructor)
CSE 124/224. (MS students are permitted to enroll in CSE 224 only)
CSE-130/230 (*Only Sections previously completed with Sorin Lerner are restricted under this policy)
CSE 150/250A **(Only sections previously completed with Lawrence Saul are restricted under this policy)
CSE 158/258
CSE 176A/276D.

Comprehensive Plan: Capstone

Comp Exam Guidelines

Per this plan, the student must pass the coprehensive examinations designed to test the student’s knowledge in fundamental computer science material. The comprehensive exam is a practical exam designed to evaluate each student's ability to apply what they have learned. In order to ensure that the exam is relevant and presented in context, it is integrated into host courses.