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Computer Engineering B.Comp.E.

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College of Science and Engineering (TIOT)261 - Bachelor of Computer Engineering

Description

Program details

Admission details

Requirements

Contact information

Sample plan

Learning outcomes

Completion requirement

Students should complete one of the two bullet point options: either a single 4-credit MATH course or both 2-credit UMTMP courses.

Complete 1 - 2 course(s) and earn exactly 4 credit(s) from the following:
  • 0036951
    OR     0002821
    OR     0036981
  • 7991841
    AND     0148631
Completion requirement
Complete exactly 3 course(s) and earn exactly 12 credit(s) from the following:
  • 0036721
    OR     8094491
  • 0036751
  • 0021871
Completion requirement
Complete exactly 8 course(s) and earn exactly 24 credit(s) from the following:
  • 8194131
  • 0033711
  • 0044891
  • 0044911
  • 0044951
  • 0028741
  • 8259491
  • 7932041
    OR     0021731
Completion requirement

Students must complete 28 total technical elective credits, with a minimum of 22 credits coming from the Core Depratment Electives (EE4xxx/5xxx courses and CSCI4xxx/5xxx courses).

Fulfill ALL of the following requirements:

The 22 credits of core department technical electives include EE 4xxx/5xxx level courses and CSCI 4xxx/5xxx level courses. The following three courses are excluded from the core 22 credits: CSCI 4921, EE 4981H, and EE 4982V.

Fulfill ALL of the following requirements:
Complete exactly 1 course(s) and earn exactly 4 credit(s) from the following:
  • 0048071
AND

Take 2 total EE and/or CSCI lab courses from the following list. Students who complete the Honors Project (EE 4981H and EE 4982V) only need to take 1 lab course.

Complete 2 or more course(s) and earn 2 or more credit(s) from the following:
  • 0044961
  • 8065231
  • 0045361
  • 0046311
  • 0047051
  • 0047041
  • 0046411
  • 8266601
  • 7914561
  • 7983031
  • 7914571
  • 8007501
  • 0044971
  • 0045021
  • 0034101
  • 8172391
  • 0046441
  • 0166391
  • 0032541
  • 8039021
  • 0048051
  • 8039561
  • 0021521
  • 0045181
AND

Take a total of 5 courses in four of the seven technical specialty areas. Take 1 course in three separate technical specialty areas, and take 2 courses in a fourth technical specialty area.

Complete at least 4 of the following:
Complete 1 or more course(s) and earn credit(s) from the following:
  • 7998831
  • 8168051
  • 8112421
  • 7920411
    OR     0021781
  • 0033741
  • 8017761
OR
Complete 1 or more course(s) and earn credit(s) from the following:
  • 0045351
  • 0045371
  • 0047041
  • 0046751
  • 8258251
  • 8199641
  • 8168051
  • 8039561
    OR     0021521
  • 8276871
  • 8277711
  • 7975491
  • 0167651
  • 0140901
  • 7987801
  • 0045181
  • 7949801
  • 0045191
OR
Complete 1 or more course(s) and earn credit(s) from the following:
  • 0047051
  • 0047061
  • 0168811
  • 0034071
  • 0034081
  • 0034101
  • 0137081
  • 0046201
  • 8260101
OR
Complete 1 or more course(s) and earn credit(s) from the following:
  • 0046401
  • 0033021
  • 7906541
  • 7906551
    OR     0021741
  • 7991811
  • 7973461
  • 7930781
OR
Complete 1 or more course(s) and earn credit(s) from the following:
  • 8168561
  • 0021891
  • 7906561
    OR     0021921
  • 8277981
  • 0021841
  • 7975481
  • 0037181
  • 8071601
  • 7997201
  • 0021831
  • 7973461
  • 0021451
  • 0021471
  • 0021931
  • 0043121
  • 0021861
OR
Complete 1 or more course(s) and earn credit(s) from the following:
  • 8277751
    OR     7906571
  • 8254341
  • 0046431
  • 0021631
  • 0021641
  • 0021471
  • 8028101
  • 7973471
  • 7930771
  • 8103501
OR
Complete 1 or more course(s) and earn credit(s) from the following:
  • 8277931
  • 8103621
  • 0021901
  • 8162951
  • 0021421
  • 8103481
  • 8103491
AND
Complete additional 4xxx/5xxx EE and/or 4xxx/5xxx CSCI courses to meet the minimum 22 credits of required Core Department Electives.

  • EE 4xxxx

  • EE 5xxxx

  • CSCI 4xxx

  • CSCI 5xxx

  • Excludes CSCI 4921, EE 4981H, and EE 4982V

AND

Students may complete up to 6 credits of additional approved technical electives (outside the core department electives) and apply those credits toward the overall 28 credit technical elective requirement. This list is not exhaustive. Students are encouraged to consult with their departmental advisor for additional options. Excludes CSCI 4921.

Fulfill ANY of the following requirements:
Complete course(s) and earn up to 6 credit(s) from the following:
  • 0043291
  • 0043341
  • 0043361
  • 0043401
  • 0043571
  • 0000431
  • 0040101
  • 0142901
  • 0111581
  • 8052301
  • 0169251
  • 7974281
  • 8017731
  • 8028671
  • 0042261
  • 0050111
  • jZfjCssdnISZ6GeLW2wZ
    OR     8077551
    OR     8077561
  • 8077541
  • 0023921
    OR     8081851
  • 0023931
  • 0023951
    OR     0023961
  • 0161861
  • 0162241
  • 7930761
  • 8154541
  • 8182461
  • 8153421
  • 7984611
  • 7984631
  • 0073381
  • 0052621
  • 0055341
  • 0055331
  • 0055351
  • 0076841
  • 0076861
  • 7908741
  • 0163401
  • 0049901
  • 0049961
  • 0050341
  • 7890001
  • 8199631
  • 0050401
  • 7967041
  • 7967051
  • 7967061
  • 0105111
  • 7905411
  • 8136481
  • 0020781
  • 0020951
  • 0020961
  • 0064081
  • 0064091

  • MATH4xxx

  • MATH5xxx

OR

The Co-op Program provides students with a professional work experience which takes place over two semesters. Students must complete both CSE4896 and CSE4996 in order to receive credit as additional technical electives.

Complete exactly 2 course(s) and earn exactly 4 credit(s) from the following:
  • 8252421
  • 8254361
OR
Complete course(s) and earn up to 4 credit(s) from the following:
  • 8198711
  • 8198721
  • 8025381
  • 8261291
  • 8006881
OR

The Honors Project provides students with a research experience that takes place over two semesters. Students must complete both EE4981H (fall) and EE 4982V (spring) to receive credit as additional technical electives.

Complete exactly 2 course(s) and earn exactly 4 credit(s) from the following:
  • 0048141
  • 0048151
Completion requirement

Students are required to take one upper division writing intensive course within the major. If that requirement has not been satisfied within the core major requirements, students must choose one course from the following list. Some of these courses may also fulfill other major requirements.

Complete up to 1 course(s) and earn credit(s) from the following:
  • 8259491
  • 0048071
  • 7998831
  • 8039561
  • 8162951
Completion requirements

Plan C (coursework only): maximum of 16 credits, of which 6 credits may be used for both the undergraduate and graduate degree programs

Plan A (thesis option): maximum of 14 credits, of which 6 credits may be used for both the undergraduate and graduate degree programs

  • EE 5121 - Transistor Device Modeling for Circuit Simulation

  • EE 5141 - Introduction to Microsystem Technology

  • EE 5163 - Semiconductor Properties and Devices I

  • EE 5164 - Semiconductor Properties and Devices II

  • EE 5171 - Microelectronic Fabrication

  • EE 5173 - Basic Microelectronics Laboratory

  • EE 5181 - Micro and Nanotechnology by Self Assembly

  • EE 5231 - Linear Systems and Control

  • EE 5235 - Robust Control System Design

  • EE 5239 - Introduction to Nonlinear Optimization

  • EE 5241 - Optimal Control and Reinforcement Learning

  • EE 5251 - Optimal Filtering and Estimation

  • EE 5271 - Robot Vision

  • EE 5301 - VLSI Design Automation I

  • EE 5302 - VLSI Design Automation II

  • EE 5323 - VLSI Design I

  • EE 5324 - VLSI Design II

  • EE 5327 - VLSI Design Laboratory

  • EE 5329 - VLSI Digital Signal Processing Systems

  • EE 5333 - Analog Integrated Circuit Design

  • EE 5334 - CMOS VLSI Data Converter Design

  • EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing

  • EE 5351 - Applied Parallel Programming

  • EE 5355 - Algorithmic Techniques for Scalable Many-core Computing

  • EE 5361 - Computer Architecture

  • EE 5364 - Advanced Computer Architecture

  • EE 5371 - Computer Systems Performance Measurement and Evaluation

  • EE 5373 - Data Modeling Using R

  • EE 5389 - Introduction to Predictive Learning

  • EE 5393 - Circuits, Computation, and Biology

  • EE 5501 - Digital Communication

  • EE 5505 - Wireless Communication

  • EE 5521 - Intro to Machine Learning and Data Science for Electrical and Computer Engineers & Roboticists

  • EE 5531 - Probability and Stochastic Processes

  • EE 5542 - Adaptive Digital Signal Processing

  • EE 5545 - Digital Signal Processing Design (computer based)

  • EE 5549 - Digital Signal Processing Structures for VLSI

  • EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence

  • EE 5571 - Statistical Learning and Inference

  • EE 5581 - Information Theory and Coding

  • EE 5583 - Error Control Coding

  • EE 5585 - Data Compression

  • EE 5601 - Introduction to RF/Microwave Engineering

  • EE 5602 - RF/Microwave Circuit Design

  • EE 5607 - Wireless Hardware System Design

  • EE 5611 - Plasma-Aided Manufacturing

  • EE 5613 - RF/Microwave Circuit Design Laboratory

  • EE 5616 - Antennas: Theory, Analysis, and Design

  • EE 5621 - Physical Optics

  • EE 5622 - Physical Optics Laboratory

  • EE 5624 - Optical Electronics

  • EE 5627 - Optical Fiber Communication

  • EE 5640 - Introduction to Nano-Optics.

  • EE 5649 - Infrared Devices and Technology

  • EE 5653 - Physical Principles of Magnetic Materials

  • EE 5655 - Magnetic Recording

  • EE 5657 - Physical Principles of Thin Film Technology

  • EE 5670 - Spintronic Devices

  • EE 5705 - Electric Drives in Sustainable Energy Systems

  • EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory

  • EE 5721 - Power Generation Operation and Control

  • EE 5741 - Advanced Power Electronics

  • EE 5745 - Wind Energy Essentials

  • EE 5811 - Biological Instrumentation

Plan C (coursework only)

  • Non-EE 4000 and 5000 level coursework--maximum of 12 credits total.

  • EE 4000 level EE coursework--maximum of 6 credits.

  • Note: students may take a maximum of 9 credits of 4000 level courses (EE and non-EE combined).

Plan A (thesis option)

  • Non-EE 4000 and 5000 level coursework--maximum of 6 credits total.

  • EE 4000 level EE coursework--maximum of 6 credits.

  • Note: students may take a maximum of 9 credits of 4000 level courses (EE and non-EE combined).

Non-EE 5000 level courses

  • AEM 5247 - Hypersonic Aerodynamics

  • AEM 5253 - Computational Fluid Mechanics

  • AEM 5333 - Design-to-Flight: Small Uninhabited Aerial Vehicles

  • AEM 5401 - Intermediate Dynamics

  • AEM 5431 - Trajectory Optimization

  • AEM 5501 - Continuum Mechanics

  • AEM 5503 - Theory of Elasticity

  • AEM 5581 - Mechanics of Solids

  • AEM 5651 – Aeroelasticity

  • BBE 5023 - Process Control and Instrumentation

  • BBE 5333 - Off-Road Vehicle Design

  • BBE 5413 - A Systems Approach to Residential Construction

  • BBE 5416 - Building Testing & Diagnostics

  • BBE 5733 - Renewable Energy Technologies

  • BioC 5001 - Biochemistry and Cellular Biology

  • BioC 5361 - Microbial Genomics and Bioinformatics

  • BioC 5527 - Introduction to Modern Structural Biology

  • BioC 5528 - Spectroscopy and Kinetics

  • BioC 5001 - Biochemistry and Cellular Biology

  • BIOL 5272 - Applied Biostatistics

  • BIOL 5485 - Introductory Bioinformatics

  • BMEN 5001 - Advanced Biomaterials

  • BMEN 5041 - Tissue Engineering

  • BMEN 5101 - Advanced Bioelectricity and Instrumentation

  • BMEN 5111 - Biomedical Ultrasound

  • BMEN 5151 - Introduction to BioMEMS and Medic Microdevices

  • BMEN 5201 - Advanced Biomechanics

  • BMEN 5311 - Advanced Biomedical Transport Processes

  • BMEN 5321 - Microfluidics in Biology and Medicine

  • BMEN 5351 - Cell Engineering

  • BMEN 5401 - Advanced Biomedical Imaging

  • BMEN 5411 - Neural Engineering

  • BMEN 5412 - Neuromodulation

  • BMEN 5413 - Neural Decoding and Interfacing

  • BMEN 5421 - Introduction to Biomedical Optics

  • BMEN 5444 - Muscle

  • BMEN 5501 - Biology for Biomedical Engineers

  • BMEN 5701 - Cancer Bioengineering

  • CHEM 5755 - X-Ray Crystallography

  • CHEM 5210 - Materials Characterization 

  • CHEN 5751 - Biochemical Engineering

  • CHEN 5753 - Biological Transport Processes

  • CHEN 5771 - Colloids and Dispersions

  • CE 5211 - Traffic Engineering

  • CE 5214 - Transportation Systems Analysis

  • CE 5341 -Wave Methods for Nondestructive Testing

  • CE 5411 -Applied Structural Mechanics

  • CMB 5200 - Statistical Genetics and Genomics

  • CSCI 5103 - Operating Systems

  • CSCI 5105 - Introduction to Distributed Systems

  • CSCI 5106 - Programming Languages

  • CSCI 5115 - User Interface Design, Implementation and Evaluation

  • CSCI 5125 - Collaborative and Social Computing

  • CSCI 5143 - Real-Time and Embedded Systems

  • CSCI 5161 - Introduction to Compilers

  • CSCI 5211 - Data Communications and Computer Networks

  • CSCI 5221 - Foundations of Advanced Networking

  • CSCI 5231 -Wireless and Sensor Networks

  • CSCI 5271 - Introduction to Computer Security

  • CSCI 5302 - Analysis of Numerical Algorithms

  • CSCI 5304 - Computational Aspects of Matrix Theory

  • CSCI 5403 - Computational Complexity

  • CSCI 5421 - Advanced Algorithms and Data Structures

  • CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming

  • CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics

  • CSCI 5471 - Modern Cryptography

  • CSCI 5481 - Computational Techniques for Genomics

  • CSCI 5511 - Artificial Intelligence I

  • CSCI 5512 - Artificial Intelligence II

  • CSCI 5521 - Introduction to Machine Learning

  • CSCI 5523 - Introduction to Data Mining

  • CSCI 5525 - Machine Learning

  • CSCI 5527 - Deep Learning: Models, Computation, and Applications 

  • CSCI 5541 - Natural Language Processing

  • CSCI 5551 - Introduction to Intelligent Robotic Systems

  • CSCI 5552 - Sensing and Estimation in Robotics

  • CSCI 5561 - Computer Vision

  • CSCI 5607 - Fundamentals of Computer Graphics 1

  • CSCI 5608 - Fundamentals of Computer Graphics II

  • CSCI 5609 - Visualization

  • CSCI 5611 - Animation & Planning in Games

  • CSCI 5619 - Virtual Reality and 3D Interaction

  • CSCI 5707 - Principles of Database Systems

  • CSCI 5708 - Architecture and Implementation of Database Management Systems

  • CSCI 5801 - Software Engineering I

  • CSCI 5802 - Software Engineering II

  • ESCI 5201 - Time-Series Analysis of Geological Phenomena

  • ESCI 5204 - Geostatistics and Inverse Theory

  • ESCI 5205 - Fluid Mechanics in Earth and Environmental Sciences

  • ESCI 5302 - Isotope Geology

  • ESCI 5353 - Electron Microprobe Theory and Practice

  • GCD 5036 - Molecular Cell Biology

  • IE 5111 - Systems Engineering I

  • IE 5112 - Introduction to Operations Research

  • IE 5113 - Systems Engineering II

  • IE 5441 - Financial Decision Making

  • IE 5285 - Engineering the Allocation of Public Resources

  • MATS 5517 - Electron Microscopy

  • MATS 5531 - Electrochemical Engineering

  • MATS 5771 - Colloids and Dispersions

  • MATS 5802 Machine Learning 

  • MATH 5067 - Actuarial Mathematics I

  • MATH 5068 - Actuarial Mathematics II

  • MATH 5075 - Mathematics of Options, Futures, and Derivative Securities I

  • MATH 5076 - Mathematics of Options, Futures, and Derivative Securities II

  • MATH 5165 - Mathematical Logic I

  • MATH 5166 - Mathematical Logic II

  • MATH 5248 - Cryptology and Number Theory

  • MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves

  • MATH 5335 - Geometry I

  • MATH 5336 - Geometry II

  • MATH 5378 - Differential Geometry

  • MATH 5385 - Introduction to Computational Algebraic Geometry

  • MATH 5445 - Mathematical Analysis of Biological Networks

  • MATH 5447 - Theoretical Neuroscience

  • MATH 5467 - Introduction to the Mathematics of Image and Data

  • MATH 5485 - Introduction to Numerical Methods I

  • MATH 5486 - Introduction to Numerical Methods II

  • MATH 5525 - Introduction to Ordinary Differential Equations

  • MATH 5535 - Dynamical Systems and Chaos

  • MATH 5583 - Complex Analysis

  • MATH 5587 - Elementary Partial Differential Equations I

  • MATH 5588 - Elementary Partial Differential Equations II

  • MATH 5615H - Introduction to Analysis 

  • MATH 5651 - Basic Theory of Probability and Statistics

  • MATH 5652 - Introduction to Stochastic Processes

  • MATH 5654 - Prediction and Filtering

  • MATH 5705 - Enumerative Combinatorics

  • MATH 5707 - Graph Theory and Non-enumerative Combinatorics

  • MATH 5711 - Linear Programming and Combinatorial Optimization

  • ME 5113 - Aerosol/Particle Engineering

  • ME 5223 - Materials in Design

  • ME 5228 - Introduction to Finite Element Modeling, Analysis, and Design

  • ME 5229 - Finite Elements and Numerical Methods for Computational Mechanics

  • ME 5241 - Computer-Aided Engineering

  • ME 5243 - Advanced Mechanism Design

  • ME 5247 - Stress Analysis, Sensing, and Transducers

  • ME 5281 - Analog and Digital Control

  • ME 5286 - Robotics

  • ME 5312 - Solar Thermal Technologies

  • ME 5341 - Case Studies in Thermal Engineering and Design 

  • ME 5344 - Thermodynamics of Fluid Flow With Applications

  • ME 5351 - Computational Heat Transfer

  • ME 5461 - Internal Combustion Engines

  • MOT 5001 - Leadership, Professionalism and Business Basics for Engineers (MOT 5001 counts only toward additional coursework credits, seminar/directed study 2 credit rule)

  • MPHY 5170 - Basic Radiological Physics

  • MPHY 5171 - Medical and Health Physics of Imaging I

  • MPHY 5174 - Medical and Health Physics of Imaging II

  • MPHY 5178 - Physical Principles of Magnetic Resonance Imaging

  • NSC 5040 - Brain Networks: From Connectivity to Dynamics

  • NSE 5202 - Theoretical Neuroscience: Systems and Information Processing

  • NSE 5203 - Neuroscience of Vision

  • NSE 5661 - Systems Neuroscience

  • PHSL 5061 - Physiology for Biomedical Engineers

  • PHSL 5101 - Human Physiology

  • PHSL 5201 - Computational Neuroscience I: Membranes and Channels

  • PHYS 5001 - Quantum Mechanics I

  • PHYS 5002 - Quantum Mechanics II

  • PHYS 5011 - Classical Physics I

  • PHYS 5012 - Classical Physics II

  • PHYS 5041 - Mathematical Methods for Physics

  • PHYS 5081 - Introduction to Biopolymer Physics

  • PHYS 5201 - Thermal and Statistical Physics

  • PHYS 5402 - Radiological Physics

  • PHSL 5510 - Advanced Cardiac Physiology and Anatomy

  • PHYS 5701 - Solid-State Physics for Engineers and Scientists

  • PHYS 5702 - Solid State Physics for Engineers and Scientists

  • PSY 5036W - Computational Vision (WI)

  • PSY 5038W - Introduction to Neural Networks (WI)

  • STAT 5021 - Statistical Analysis

  • STAT 5031 - Statistical Methods for Quality Improvement

  • STAT 5041 - Bayesian Decision Making

  • STAT 5101 - Theory of Statistics I

  • STAT 5102 - Theory of Statistics II

  • STAT 5201 - Sampling Methodology in Finite Populations

  • STAT 5302 - Applied Regression Analysis

  • STAT 5303 - Designing Experiments

  • STAT 5401 - Applied Multivariate Methods

  • STAT 5421 - Analysis of Categorical Data

  • STAT 5511 - Time Series Analysis

Non-EE 4000 level courses

  • AEM 4203 - Aerospace Propulsion

  • AEM 4295 - Problems in Fluid Mechanics

  • AEM 4301 - Orbital Mechanics

  • AEM 4303W - Flight Dynamics and Control (WI)

  • AEM 4305 - Spacecraft Attitude Dynamics and Control

  • AEM 4331 - Aerospace Vehicle Design

  • AEM 4333 - Aerospace Design: Special Projects

  • AEM 4371 - Helicopter Aerodynamics

  • AEM 4495 - Problems in Aerospace Systems

  • AEM 4501 - Aerospace Structures

  • AEM 4502 - Computational Structural Analysis

  • AEM 4511 - Mechanics of Composite Materials

  • AEM 4581 - Mechanics of Solids

  • AEM 4595 - Problems in Mechanics and Materials

  • AEM 4601 - Instrumentation Laboratory

  • AEM 4602W - Aeromechanics Laboratory (WI)

  • BIOL 4003 - Genetics

  • BIOL 4004 - Cell Biology

  • BIOL 4035 - Metagenomics Laboratory

  • BIOL 4121 - Microbial Ecology and Applied Microbiology

  • BIOL 4700 - Cell Physiology

  • BIOL 4850 - Special Topics in Biology

  • BIOL 4862 - Biological Photography and Digital Imaging Techniques

  • BIOL 4950 - Special Topics in Biology

  • CHEM 4001 - Chemistry of Biomass and Biomass Conversion to Fuels and Products

  • CHEM 4011 - Mechanisms of Chemical Reactions

  • CHEM 4021 - Computational Chemistry

  • CHEM 4066 - Chemistry of Industry

  • CHEM 4101 - Modern Instrumental Methods of Chemical Analysis

  • CHEM 4111W - Modern Instrumental Methods of Chemical Analysis Lab (WI)

  • CHEM 4201 - Materials Chemistry

  • CHEM 4214 - Polymers

  • CHEM 4221 - Introduction to Polymer Chemistry

  • CHEM 4223W - Polymer Laboratory (WI)

  • CHEM 4301 - Applied Surface and Colloid Science

  • CHEM 4311W - Advanced Organic Chemistry Lab (WI)

  • CHEM 4321 - Organic Synthesis

  • CHEM 4322 - Advanced Organic Chemistry

  • CHEM 4352 - Physical Organic Chemistry

  • CHEM 4361 - Interpretation of Organic Spectra

  • CHEM 4411 - Introduction to Chemical Biology

  • CHEM 4412 - Chemical Biology of Enzymes

  • CHEM 4413 - Nucleic Acids

  • CHEM 4501 - Introduction to Thermodynamics, Kinetics, and Statistical Mechanics

  • CHEM 4502 - Introduction to Quantum Mechanics and Spectroscopy

  • CHEM 4511W - Advanced Physical Chemistry Lab (WI)

  • CHEM 4601 - Green Chemistry (ENV)

  • CHEM 4701 - Inorganic Chemistry

  • CHEM 4711W - Advanced Inorganic Chemistry Lab (WI)

  • CHEM 4715 - Physical Inorganic Chemistry

  • CHEM 4725 - Organometallic Chemistry

  • CHEM 4735 - Bioinorganic Chemistry

  • CHEM 4745 - Advanced Inorganic Chemistry

  • CHEN 4214 - Polymers

  • CHEN 4401W - Senior Chemical Engineering Lab (WI)

  • CHEN 4402W - Chemical Engineering Lab II (WI)

  • CHEN 4501W - Chemical Engineering Design I (WI)

  • CHEN 4502W - Chemical Engineering Design II (WI)

  • CHEN 4601 - Process Control

  • CHEN 4701 - Advanced Undergraduate Applied Math I: Linear Analysis

  • CHEN 4702 - Advanced Undergraduate Rheology

  • CHEN 4704 - Advanced Undergraduate Physical Rate Processes I: Transport

  • CHEN 4706 - Advanced Undergraduate Physical and Chemical Thermodynamics

  • CHEN 4707 - Advanced Undergraduate Statistical Thermodynamics and Kinetics

  • CHEN 4708 - Advanced Undergraduate Chemical Rate Processes: Analysis of Chemical Reactors

  • CHEN 4712 - Rheology Laboratory Project

  • CSCI 4011 - Formal Languages and Automata Theory

  • CSCI 4041 - Algorithms and Data Structures

  • CSCI 4041H - Algorithms and Data Structures

  • CSCI 4061 - Introduction to Operating Systems

  • CSCI 4107 - Introduction to Computer Graphics Programming

  • CSCI 4131 - Internet Programming

  • CSCI 4211 - Introduction to Computer Networks

  • CSCI 4511W - Introduction to Artificial Intelligence (WI)

  • CSCI 4611 - Programming Interactive Computer Graphics and Games

  • CSCI 4707 - Practice of Database Systems

  • CSCI 4921 - History of Computing (TS, HIS)

  • CSCI 4970W - Advanced Project Laboratory (WI)

  • MATH 4065 - Theory of Interest

  • MATH 4152 - Elementary Mathematical Logic

  • MATH 4242 - Applied Linear Algebra

  • MATH 4281 - Introduction to Modern Algebra

  • MATH 4428 - Mathematical Modeling

  • MATH 4512 - Differential Equations with Applications

  • MATH 4567 - Applied Fourier Analysis

  • MATH 4603 - Advanced Calculus I

  • MATH 4604 - Advanced Calculus II

  • MATH 4606 - Advanced Calculus

  • MATH 4653 - Elementary Probability

  • MATH 4707 - Introduction to Combinatorics and Graph Theory

  • MATH 4990 - Topics in Mathematics

  • PHYS 4001 - Analytical Mechanics

  • PHYS 4002 - Electricity and Magnetism

  • PHYS 4041 - Computational Methods in the Physical Sciences

  • PHYS 4051 - Methods of Experimental Physics I

  • PHYS 4052W - Methods of Experimental Physics II (WI)

  • PHYS 4071 - Concepts in Physics

  • PHYS 4101 - Quantum Mechanics

  • PHYS 4121W - History of 20th-Century Physics (WI)

  • PHYS 4201 - Statistical and Thermal Physics

  • PHYS 4211 - Introduction to Solid-State Physics

  • PHYS 4303 - Electrodynamics and Waves

  • PHYS 4511 - Introduction to Nuclear and Particle Physics

  • PHYS 4611 - Introduction to Space Physics

  • PHYS 4621 - Introduction to Plasma Physics

  • PHYS 4911 - Introduction to Biopolymer Physics

  • STAT 4101 - Theory of Statistics I

  • STAT 4102 - Theory of Statistics II

  • STAT 4931 - Topics in Statistics

  • STAT 4932 - Topics in Statistics

EE 4000 level courses

  • EE 4111 - Advanced Analog Electronics Design

  • EE 4161W - Energy Conversion and Storage [WI] 

  • EE 4163 - Energy Conversion and Storage Laboratory

  • EE 4231 - Linear Control Systems: Designed by Input/Output Methods

  • EE 4233 - State Space Control System Design

  • EE 4235 - Linear Control Systems Laboratory

  • EE 4237 - State Space Control Laboratory

  • EE 4301 - Digital Design With Programmable Logic

  • EE 4303 - Introduction to Programmable Devices Laboratory

  • EE 4341 - Embedded System Design

  • EE 4363 - Computer Architecture and Machine Organization

  • EE 4389W - Introduction to Predictive Learning [WI]

  • EE 4501 - Communications Systems

  • EE 4505 - Communications Systems Laboratory

  • EE 4521 - Introduction to Machine Learning and Data Science for Electrical and Computer Engineers

  • EE 4541 - Digital Signal Processing

  • EE 4545 - Real-time Signal Processing Laboratory

  • EE 4607 - Wireless Hardware System Design

  • EE 4616 - Antennas: Theory, Analysis, and Design

  • EE 4623 - Introduction to Modern Optics

  • EE 4701 - Electric Drives

  • EE 4703 - Electric Drives Laboratory

  • EE 4721 - Introduction to Power System Analysis

  • EE 4722 - Power System Analysis Laboratory

  • EE 4741 - Power Electronics

  • EE 4743 - Switch-Mode Power Electronics Laboratory

The competitive edge offered by the IDP is especially important in the area of computer engineering, where MS degrees are highly valued. The complexity and sophistication of many computer engineering subfields require advanced study and many employers expect their new employees to have this increased level of training. As a result, many students enter as freshmen planning to obtain an MS degree. The Integrated Degree Program route to an MS degree will draw more prospective undergraduates with plans to become computer engineers. Likewise, the proposed program is likely to entice current undergraduates to stay at the University of Minnesota for their MS degrees, benefiting our surrounding professional community.

By offering the Integrated Degree Program, computer engineering students can streamline their education in a condensed time frame allowing them to accelerate their entry into high paying jobs. Graduates of the Integrated Degree Program will have a competitive advantage over other applicants due to the greater depth of training in advanced electrical and computer engineering courses.

Link to Bachelor of Computer Engineering degree program.

Link to Master’s of Electrical and Computer Engineering degree program.

Link to Integrated Bachelor of Electrical Engineering/Master of Science in Electrical and Computer Engineering degree program.

Additional information and details about the BCompE/MSECE IDP can be found on the ECE undergraduate advising website: the ECE Matrix.

Admission

Eligibility Requirements

  • Applicants must be enrolled University of Minnesota Twin Cities students admitted to the Bachelor of Computer Engineering undergraduate program.

  • Applicants to the integrated program are not required to take the GRE.

  • BCompE students are eligible to apply after they have completed the following courses:

    • EE 3015

    • EE 3101

    • EE 3115, and 

    • a minimum of three additional credits of EE 3xxx or EE 4xxx level coursework.

  • Students who are in their final semester as an undergraduate BCompE student are ineligible to apply for the integrated degree program.

  • Applicants with a technical GPA minimum of 3.4 (as defined by the College of Science and Engineering) are automatically admitted to the IDP provided they meet the eligibility requirements.

  • Applicants whose technical GPA falls between 3.2 technical and 3.4 are admitted on a case by case basis with more weight given to sophomore and junior year EE coursework.

Application Timing

Students may apply to the BCompE/MSECE IDP when they have completed the courses required for admission to the IDP, generally during the spring semester of junior year. This timing provides the student one year (two semesters) to complete undergraduate degree requirements and take graduate level courses. Students may also apply to the BCompE/MSECE IDP during the fall of their senior year which provides the student one semester to complete undergraduate degree requirements and take graduate level courses. Second semester seniors are ineligible for the BCompE/MSECE IDP but can apply to the MSECE program through the regular MS application process.

Application Procedure

Students apply to the BCompE/MSECE IDP directly in the ECE Department using a simplified application process.

  • Students must submit the MSECE IDP application form which is available on the ECE Matrix website.

  • Students must include a shortened statement of purpose. 

  • GRE test scores are not required.

  • Letters of recommendation are not required. 

  • Students do not have to pay a fee to submit an IDP application to the ECE Department. 

  • Once a student is admitted to the IDP, then they will need to follow the instructions for confirming their admission and participation in the IDP by applying to the Graduate School and paying the UMTC graduate school application fee. Students will receive instructions from the ECE graduate advisor about the confirmation process.

Application Deadlines

Fall Admission

  • Applications open on January 15

  • Final application deadline is March 15

Spring Admission

  • Applications open on August 15

  • Final application deadline is October 15

Application Review

The applications are reviewed by the ECE undergraduate advisor and the ECE graduate advisor.

Application Decisions

Students will be notified of their admission decision via email by the ECE graduate advisor. 

In cases where the student’s technical GPA falls between 3.2 and 3.4, the decision to admit or deny may be deferred until the current semester grades have been posted to the student’s record. In those cases, the student will receive a final decision to admit or deny before the beginning of the next semester.

Undergraduate Degree Progress

The ECE undergraduate advisor will advise all IDP students on degree planning during their undergraduate education. The ECE Department requires all undergraduates (including IDP students) to meet once per semester with the ECE undergraduate advisor for course planning and review of degree requirements. During these advising sessions, the ECE undergraduate advisor will review the student’s APAS and make necessary adjustments as needed.

IDP students are encouraged to meet with the ECE graduate advisor if they have questions about applicability of MSECE courses, the MSECE degree program (e.g., Plan A versus Plan C programs), or any other MSECE specific questions.

At the beginning of each semester, all IDP undergraduates must notify the ECE undergraduate advisor regarding which courses the student would like to count toward the MSECE degree. The ECE undergraduate advisor will be responsible for moving those courses to the IDP subplan no later than the 10th day of class for the semester.

Once admitted to the IDP program, undergraduate students are expected to maintain a minimum of a 3.0 technical GPA through completion of their BCompE degree. Students who do not maintain the 3.0 technical GPA may have their admission to the IDP rescinded.

Students admitted to this IDP will complete and be awarded the undergraduate degree within 4 years (8 semesters) for NHS and 3 years (6 semesters) for NAS students. Admission into the IDP will be revoked if the awarding of this bachelor’s degree exceeds 8 semesters for NHS students and 6 semesters for NAS students.

The associated BCompE degree must be awarded prior to the beginning of the student’s MSECE graduate career. Bachelor’s and master’s degrees cannot be awarded simultaneously.

Admission to the graduate program is guaranteed provided the student continues to maintain a 3.0 or higher technical GPA in all semesters leading up to completion of the BCompE degree.

During their final semester as an undergraduate, students are responsible to complete the steps required to transition to graduate student status. This information is detailed on the ECE Matrix.

The CSE Undergraduate Records and Curriculum Team is responsible for clearing the bachelor’s degree.

Graduate Degree Progress

Once enrolled in the master’s degree, students must complete a minimum of one semester and 14 course credits as a registered graduate student, and students must complete all master’s degree requirements before the awarding of the master’s degree.

Credit Transfer

Students can apply a maximum of 16 credits taken as an undergraduate towards their master's degree. Up to 6 of the credits can be double counted for both the undergraduate BCompE degree and the graduate MSECE degree.

The MSECE degree has two options: Plan A (thesis option) or Plan C (coursework-only option). Both MSECE degree options require 30 credits. IDP students may take up to 16 credits of MSECE coursework as an undergraduate. There are slight differences in the transferability of MSECE credits depending on whether the undergraduate IDP student plans to pursue Plan A or Plan C. By default, IDP students are initially put on Plan C (coursework only) upon admission. If a student prefers to do a thesis (Plan A), they should consult with the ECE graduate advisor for more specific information about Plan A requirements and transferability of coursework.

Students are expected to be familiar with the MSECE degree requirements in the ECE Graduate Handbook. Students with questions regarding the applicability of coursework to the MSECE degree should consult with the ECE graduate advisor.

The following courses have 4xxx and 5xxx level equivalents. If a student has taken the 4xxx level course as an undergraduate, the student cannot later take the 5xxx level course and count it toward the graduate degree:

  • EE 4521 and EE 5521

  • EE 4607 and EE 5607

  • EE 4616 and EE 5616

  • MOT 4001 and MOT 5001

8000 level EE courses: Although MSECE students may apply 8000 level EE courses toward the MSECE, IDP students are advised to wait until they have transitioned to graduate student status before taking 8000 level EE courses. IDP students who wish to register for 8000 level EE classes as an undergraduate should consult first with the ECE graduate advisor to determine whether a course will apply toward their MSECE. Then they must obtain prior approval via email of the Director of Undergraduate Studies and the 8000 level EE course instructor. Any 8000 level EE course will need to be added via special exception to the student’s APAS report by the ECE undergraduate advisor.

4000 level rule: a maximum of 9 credits of 4000-level courses may be used to satisfy master’s degree requirements. Of these, only 6 credits may be from the limited 4000 level EE coursework list. No 4000-level seminars, projects or directed study courses can be used. More information about the 4000 level rule can be found in the ECE Graduate Handbook.

Credits for a single course cannot be split between the undergraduate degrees, majors, or minors and graduate degree programs.

Courses that will be used to fulfill the master’s degree requirements must appear in the undergraduate degree sub-plan by the tenth day of the semester in which the student is enrolled in the courses. Any final edits or updates to this sub-plan must be reflected on the APAS no later than the last day of instruction in the semester in which the undergraduate degree will be awarded.  Courses not in this sub-plan by that time cannot be updated later and, therefore, will not be eligible for use towards the master’s degree.

Completion requirements

The Department of Computer Science & Engineering offers an integrated Bachelor's and Master's Degree program. Students accepted to the integrated program will be guaranteed admission to the Computer Science MS as long as they complete their undergraduate program. Accepted students will not need to take the GRE exam as part of their graduate application, unlike other students applying to our graduate programs.

Applicants must be enrolled University of Minnesota Twin Cities students admitted to a Computer Science or Computer Engineering undergraduate program. Applicants must meet a Technical GPA minimum of 3.5 (as defined by the College of Science & Engineering) or they must have a strong recommendation from a Computer Science and Engineering faculty member or instructor (not an ECE Faculty member).

Applicants must have at least 75 credits completed at the time of their application.
Applicants must have passed with a C- or better all of the following courses:

  • CSCI 1933 or 1913

  • CSCI 2011

  • EE 2361

  • CSCI 2033 or a math course containing linear algebra content (e.g., MATH2373, MATH2243, or MATH1572H)

  • CSCI 4041 and CSCI 4061 (applicants can have one of these courses in progress at the time of application)

Full application instructions can be found at cs.umn.edu/integrated

Courses that will be used to fulfill Master's degree requirements must appear in this sub-plan by the tenth day of the semester in which the student is enrolled in the courses.

Any final edits or updates to this sub-pan must be reflected on the APAS no later than the last day of instruction in the semester in which the undergraduate degree will be awarded. Courses not in this sub-plan by that time cannot be updated at a later time; and, therefore will not be eligible for use towards the Master's degree.

Students can transfer a maximum of 16 credits to the graduate program taken during their integrated senior undergraduate year. Students must spend a minimum of two semesters as a graduate student after the completion of their undergraduate degree. Coursework applied to the graduate degree must be taken at the graduate level (i.e., 5xxx or above) Credits being applied to the Computer Science Master’s taken while the student is an undergraduate for use in the integrated program can also be applied later to a Computer Science Ph.D. within our department if a student applies and is admitted. Credits cannot also be applied to the undergraduate degree (i.e., no "double dipping").

Students should consider taking the following courses/requirements to apply to their graduate degree as an undergraduate integrated program student (16 credits max):

  • CSCI 8970 - Computer Science Colloquium (1 credit)

  • Course to meet the Theory and Algorithms Breadth requirement (3 credits)*

  • Course to meet the Architecture, Systems, & Software Breadth requirement (3 credits)*

  • Course to meet the Applications Breadth requirement (3 credits)*

  • CSCI 5XXX level course that fits your interests and background (3 credits) or an approved graduate level elective or graduate minor course. We recommend waiting to take CSCI 8XXX level courses for your graduate year, but this level of coursework is still available to you if you have the appropriate prerequisites.

  • CSCI 5XXX level course that fits your interests and background (3 credits) or an approved graduate level elective or graduate minor course. We recommend waiting to take CSCI 8XXX level courses for your graduate year, but this level of coursework is still available to you if you have the appropriate prerequisites.

*Please refer to the Department of Computer Science & Engineering webpage for more details on which courses count for specific breadth requirements.

Completion requirement
Earn at least 4 credits from the following:
  • 0036921
    OR     0001201
    OR     0036961
    OR     0148561

MATH1471 is a 2-credit course. Students who completed this course will need to work with a CSE advisor to discuss any credit discrepancies.

Earn at least 4 credits from the following:
  • 0036931
    OR     0001211
    OR     0036971
    OR     0148611

MATH1472 is a 2-credit course. Students who completed this course will need to work with a CSE advisor to discuss any credit discrepancies.

Students should complete one of the two bullet point options: either a single 4-credit MATH course or both 2-credit UMTMP courses.

Earn at least 4 credits from the following:
  • 0036941
    OR     0002801
    OR     0043111
  • 7909141
    AND     8064971
Complete exactly 1 course(s) and earn exactly 4 credit(s) from the following:
  • 0020701
    OR     0020731
    OR     8179641

To ensure an understanding of key concepts, PHYS1301W or PHYS1401V are preferred.

Complete exactly 1 course(s) and earn exactly 4 credit(s) from the following:
  • 0020711
    OR     0020741
    OR     8179651

To ensure an understanding of key concepts, PHYS1302W or PHYS1402V are preferred.

Note: EE1301 and CSCI1113 are strongly preferred as the introductory programming course (C/C++ programming).

Complete exactly 4 course(s) and earn exactly 16 credit(s) from the following:
  • 0166341
    OR     0036691
    OR     8096661
    OR     8110721
  • 8189431
  • 0027391
  • 8096671
    OR     8103461
    OR     8166631
Completion requirement

Students should take the Robotics Colloquium course during their first semester in the IDP program.

Students should take at least one course from each of the three Key Areas (Cognition, Perception, and Robotic Modeling and Control) before moving onto Approved Electives coursework.

ROBOTICS COLLOQUIUM:

Students should take the colloquium course during their first semester in the IDP program.

  • ROB 8970 - Robotics Colloquium

KEY AREA COURSEWORK:

Students should take at least one course from each of the three Key Areas (Cognition, Perception, and Robotic Modeling and Control) before moving onto Approved Electives coursework.

  • Cognition

    • CSCI 5511 - Artificial Intelligence I

    • CSCI 5512 - Artificial Intelligence II

    • CSCI 5521 - Intro to Machine Learning

    • CSCI 5525 - Machine Learning: Analysis and Methods

    • EE 5521 - Machine Learning & Data Science for ECE and ROB Students (3 cr)

  • Perception

    • CSCI 5561 - Computer Vision 

    • EE 5271 - Robot Vision

    • EE 5561 - Image Processing and Applications

  • Robotic Modeling and Control

    • CSCI 5551 - Introduction to Intelligent Robotic Systems

    • CSCI 5552 - Sensing/Estimation in Robotics

    • ME 5286 - Robotics

    • EE 5231/AEM 5321 Linear Systems and Optimal Control/Modern Feedback Control

APPROVED ELECTIVES:

Students may start to take approved electives from the following list after completing the Robotics Colloquium and three Key Area courses or while concurrently enrolled.

  • Aerospace Engineering and Mechanics

    • AEM 5321 - Modern Feedback Control (Joint with EE 5231)

    • AEM 5333 - Design-to-Flight: Small Uninhabited Aerial Vehicles

    • AEM 5451 - Optimal Estimation (Joint with EE 5251)

    • AEM 8411 - Advanced Dynamics

    • AEM 8421 - Robust Control (Joint with EE 5235)

    • AEM 8423 - Convex Optimization in Control

    • AEM 8495 - Advanced Topics in Aerospace Systems (Topics Course)

  • Biomedical Engineering

    • BMEN 5151 - Introduction to BioMEMS and Medical Microdevices

    • BMEN 5411 - Neural Engineering

    • BMEN 5413 - Neural Interfacing

  • Computer Science

    • CSCI 5103 - Operating Systems

    • CSCI 5105 - Introduction to Distributed Systems

    • CSCI 5115 - User Interface Design, Prototyping and Evaluation

    • CSCI 5125 - Collaborative and Social Computing

    • CSCI 5143 - Real-Time and Embedded Systems

    • CSCI 5211 - Data Communications and Computer Networks

    • CSCI 5231 - Wireless and Sensor Networks

    • CSCI 5421 - Advanced Algorithms and Data Structures

    • CSCI 5451 - Introduction to Parallel Computing

    • CSCI 5481 - Computational Techniques for Genomics

    • CSCI 5511 - Artificial Intelligence I

    • CSCI 5512 - Artificial Intelligence II

    • CSCI 5521 - Intro to Machine Learning

    • CSCI 5523 - Introduction to Data Mining

    • CSCI 5525 - Machine Learning

    • CSCI 5527 - Deep Learning: Models, Computation and Applications

    • CSCI 5541 - Natural Language Processing

    • CSCI 5551 - Introduction to Intelligent Robotic Systems

    • CSCI 5552 - Sensing/Estimation in Robotics

    • CSCI 5561 - Computer Vision

    • CSCI 5563 - Multiview 3D Geometry in Computer Vision

    • CSCI 5607 - Fundamentals of Computer Graphics 1

    • CSCI 5609 - Visualization

    • CSCI 5611 - Animation and Planning in Games

    • CSCI 5619 - Virtual Reality and 3D Interaction

    • CSCI 5801 - Software Engineering I

    • CSCI 5980 - Special Topics in Computer Science (Topics Course)

    • CSCI 8211 - Advanced Computer Networks and Applications

    • CSCI 8271 - Security and Privacy in Computing

    • CSCI 8442 - Computational Geometry and Applications

    • CSCI 8523 - AI for Earth: Monitoring Changes in the Environment via Deep Learning

    • CSCI 8551 - Intelligent Agents

    • CSCI 8581 - Big Data in Astrophysics

    • CSCI 8980 - Special Advanced Topics in Computer Science (Topics Course)

  • Design

    • DES 5185 - Human Factors in Design

    • DES 5901 - Principles of Wearable Technology

    • DES 5902 - Wearable Technology Laboratory Practicum

  • Electrical Engineering

    • EE 5231 - Linear Systems and Optimal Control (Joint with AEM 5321)

    • EE 5235 - Robust Control System Design (Joint with AEM 8421)

    • EE 5239 - Introduction to Nonlinear Optimization

    • EE 5241 - Optimal Control and Reinforcement Learning

    • EE 5251 - Optimal Filtering and Estimation (Joint with AEM 5451)

    • EE 5271 - Robot Vision

    • EE 5373 - Data Modeling Using R

    • EE 5391 - Computing with Neural Networks

    • EE 5393 - Circuits, Computation, and Biology

    • EE 5505 - Wireless Communication

    • EE 5531 - Probability and Stochastic Processes

    • EE 5542 - Adaptive Digital Signal Processing

    • EE 5561 - Image Processing and Applications

    • EE 5571 - Statistical Learning and Inference

    • EE 5621/2 -  Physical Optics and Physical Optics Lab

    • EE 5624 -  Optical Electronics

    • EE 5705/7 - Electric Drives in Sustainable Energy Systems and Lab

    • EE 5940 - Special Topics in Electrical Engineering I (Topics Course)

    • EE 8215 - Nonlinear Systems

    • EE 8231 - Optimization Theory

    • EE 8581 - Detection and Estimation Theory

    • EE 8591 - Predictive Learning from Data

    • EE 8950 - Advanced Topics in Electrical and Computer Engineering

  • Finance

    • FINA 5422 - Financial Econometrics and Computational Methods I

    • FINA 5423 - Financial Econometrics and Computational Methods II

  • Human Factors

    • HUMF 5874 - Human Centered Design to Improve Complex Systems

  • Industrial Engineering

    • IE 5080 - Topics in Industrial Engineering

    • IE 5541 - Project Management

    • IE 5561 - Analytics and Data-Driven Decision Making

    • IE 5571 - Reinforcement Learning and Dynamic Programming

    • IE 8534 - Advanced Topics in Operations Research

  • Mathematics

    • MATH 5466 - Mathematics of Machine Learning and Data Analysis II

    • MATH 5707 - Graph Theory and Non-enumerative Combinatorics

  • Mechanical Engineering

    • ME 5228 - Introduction to Finite Element Modeling, Analysis, and Design

    • ME 5241 - Computer-Aided Engineering

    • ME 5243 - Advanced Mechanism Design

    • ME 5248 - Vibration Engineering

    • ME 5281 - Feedback Control Systems

    • ME 5286 - Robotics

    • ME 8243 - Topics in Design (Topics Course)

    • ME 8254 - Fundamentals of Microelectromechanical Systems (MEMS)

    • ME 8281 - Advanced Control System Design

    • ME 8282 - Advanced Control Systems Design - 2

    • ME 8283 - Design of Mechatronic Products

    • ME 8284 - Intermediate Robotics with Medical Applications

    • ME 8285 - Advanced Control System Design, with Applications to Smart Vehicles

    • ME 8345 - Computational Heat Transfer and Fluid Flow

  • Product Design

    • PDES 5704 - Computer-Aided Design Methods

  • Psychology

    • PSY 5018H - Honors Mathematical Models of Human Behavior

    • PSY 8036 - Topics in Computer Vision (Topics Course)

  • Robotics

    • ROB 5994 - Directed Research

Bachelor of Computer Engineering (BCompE) students who demonstrate strong academic performance in their BCompE degree can apply to the integrated BCompE/MSR program after meeting certain conditions. The parties to this Memorandum of Understanding (MOU) are the College of Science and Engineering, the Department of Electrical and Computer Engineering and the Minnesota Robotics Institute. This MOU documents agreements regarding admission standards, degree progress expectations, and administrative oversight, for the Integrated Bachelor of Computer Engineering/Master of Science in Robotics (BEE/MSR) program after meeting certain conditions. The integrated program benefits students by allowing BCompE students to complete some coursework for their graduate degree while completing their undergraduate program. A maximum of 16 graduate credits can be taken at the undergraduate tuition rate. Students accepted to the integrated program will be guaranteed admission to the Master of Science in Robotics after they complete their undergraduate program.

Information about the existing BCompE program and the MSR program can be found on the web at: https://cse.umn.edu/ece and https://cse.umn.edu/mnri.

Admission

Eligibility Requirements

  • Applicants must be enrolled University of Minnesota Twin Cities students admitted to the Bachelor of Computer Engineering undergraduate program.

  • Applicants to the integrated program are not required to take the GRE.

  • BCompE students are eligible to apply after they have completed the following courses:

    • EE 3015

    • EE 3101

    • EE 3115, and

    • a minimum of three additional credits of EE 3xxx or EE 4xxx coursework.

  • Students who are in their final semester as an undergraduate BCompE student are ineligible to apply for the integrated degree program.

  • Applicants must meet a technical GPA minimum of 3.4 (as defined by the College of Science and Engineering).

  • Students must have completed (or have in progress) the following undergraduate coursework prior to application:

    • MATH 1371, 1372, 2373 (or equivalent)

    • PHYS 1301W, 1302W (or equivalent)

    • EE 3025 (or equivalent) [In Progress]

    • CSCI 4041 (or equivalent) [In Progress]

Application Timing

Students apply to an integrated degree program the semester prior to the last year of undergraduate academic study, this provides the student one year (two semesters) to complete undergraduate degree requirements and take graduate level courses.

Application Procedure

Before applying, students should meet with a departmental advisor in Electrical and Computer Engineering to discuss the feasibility of completing the bachelor’s degree in four years while adding additional graduate credits in their senior year and completing the remaining Master of Science in Robotics requirements in the fifth year. Students should also meet with staff in the Minnesota Robotics Institute to further discuss the program. Students will supply the following components in their online application:

  • Resume

  • Undergraduate Transcript(s)

  • Personal Statement

    • Students will use this document to describe their interest in the robotics program,

  • their specific research interests and their technical background.

  • Letters of recommendation

    • There is no minimum requirement on the number of letters.

  • Application Fee

Application Deadlines

Deadlines for application to IDP - The Robotics M.S. program admits students for the fall semester, and only admits students to the spring semester only on a case-by-case basis. There are two application deadlines for admission to the following fall semester:

  • March 15 - priority consideration for financial support

  • May 1 - final deadline for fall admission

  • October 15 is the deadline for admission to the spring semester.

Application Review

The Robotics board of admissions (a rotating combination of faculty and staff) will handle the review and approval of applicants. Standard members include the Director of Graduate Studies, as well as various program staff members, including, but not limited to, the Graduate Program Coordinator and the Graduate Program Advisor.

Application Decisions

Depending on application materials and timing, an applicant may be asked to wait for another semester of grades before being admitted or rejected. Applications are reviewed on a rolling basis, so the date by which a student is notified of the outcome of their application will vary. At the latest, students will be notified via email by December 1 for a spring start or June 1 for a fall start. In some cases an admission decision may be deferred until semester grades are posted. Students will be notified if their application is deferred.

Undergraduate Degree Progress

The ECE Undergraduate Advisor will be responsible for handling undergraduate degree planning. Students admitted to this IDP will complete and be awarded the undergraduate degree within 4 years (8 semesters) for NHS and 3 years (6 semesters) for NAS students. Admission into the IDP will be revoked if the awarding of this bachelor’s degree exceeds 8 semesters for NHS students and 6 semesters for NAS students.

The process of approving undergraduate coursework for application to the graduate program will begin with the Robotics Graduate Program Advisor. A student who is interested in having a course approved that is not in the list of approved electives will submit an elective course approval request form. This will be reviewed by the Graduate Program Advisor, with input by the Director of Graduate Studies as necessary.

Reviewing the student’s APAS and making exceptions related to the IDP sub-plan will be handled by the Undergraduate Program Advisor. If further clarification on non-standard courses is needed, the Graduate Program Advisor will assist.

The associated bachelor’s degree must be awarded prior to the beginning of the master’s career. Bachelor’s and master’s degrees cannot be awarded simultaneously.

Admission to the MSR program is guaranteed providing the student completes the following to remain in good academic standing:

  • The BEE degree is awarded prior to beginning of the graduate career,

  • A minimum GPA of 3.0 or above is maintained in the graduate courses taken while enrolled as an undergraduate.

Students who fail to maintain these standards may have their admission to the integrated degree program rescinded.

Credit Transfer

Students can apply a maximum of 16 credits of 5000 level (or higher) courses taken as an undergraduate towards their master's degree:

  • No 4xxx-level courses can be used to fulfill graduate program requirements.

  • 4xxx/5xxx coursework: In the case that the student has taken 4xxx-level coursework during undergraduate, the 4xxx-level course cannot transfer, and the student cannot take the 5xxx-level course and count it toward the graduate degree.

    • The following courses are associated with this limitation:

      • CSCI 4511W/CSCI 5511

      • EE 4521/EE 5521

Any existing 4xxx/5xxx equivalent courses not listed above will be reviewed and enforced on a case-by-case basis.

Credits for a course cannot be split between the undergraduate degrees, majors, or minors and graduate degree programs.

Courses that will be used to fulfill the master’s degree requirements must appear in the undergraduate degree sub-plan by the tenth day of the semester in which the student is enrolled in the courses. Any final edits or updates to this sub-plan must be reflected on the APAS no later than the last day of instruction in the semester in which the undergraduate degree will be awarded. Courses not in this sub-plan by that time cannot be updated later and, therefore, will not be eligible for use towards the master’s degree.

Students should plan to take the Robotics Colloquium (ROB 8970) in their first or second semester, depending on whether they are admitted in the fall or spring.

Students should then start to take their key area courses from the key areas of Cognition, Perception, and Robot Modeling & Control.

If a student has room to take additional MS courses, then they can select from the additional coursework list.

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