List of Math, Statistics & Instrumentation Courses

Last Modified: September 9, 2021

Our Agricultural and Biological Engineering Graduate Handbook states: “One MATH course beyond differential equations from an approved list, one course in statistical design and analysis from an approved list, and one course in instrumentation and measurement from an approved list are required for the MS degree and PhD degree in ABE. If the PhD student has already taken the above courses during their Master's program, the student can instead take two courses from any of the three areas (math, stats, or instrumentation). If a course is not in this requirement list, it must be requested by the advisor and approved by the Graduate Committee or the DGS (Director of Graduate Studies) prior to taking the course. No course shall be double counted for more than on requirement. 

Math Requirement

A list of acceptable math courses is listed in Table 1. A statistics course does not meet the math course requirement even if the statistics course is offered by the Math Department. The math course requirement cannot be taken credit/no credit and a grade of C and above is required. 

Table 1. List of Math Courses for Ph.D. or M.S. credit.

Course 

Credits

Prerequisite

Title
CS 444 4 h MATH 241; one of MATH 225, MATH 257, MATH 415, MATH 416, ASRM 406, or BIOE 210; CS 225 or equivalent; one of CS 361, ECE 313, MATH 461 or STAT 400. Deep Learning for Computer Vision
CS 446 3 or 4 h CS 225; One of MATH 225, MATH 257, MATH 415, MATH 416, ASRM 406 or BIOE 210; One of CS 361, ECE 313, MATH 461 or STAT 400. Machine Learning 
MATH 416 3 or 4 h MATH 241 or consent
of instructor 		 Abstract Linear Algebra 

MATH 442

3 or 4 h

MATH 385 or 441

Intro to Partial Differential Equations

MATH 489

3 or 4 h

MATH 385 or 441

Differential Equations II

MATH 553

4 h

Consent of instructor

Partial Differential Equations

MATH 450 or CS 450
or ECE 491 or CSE 401

3 or 4 h

MATH 415, MATH 385, 386,
or 441 or consent

Intro to Numerical Analysis

MATH 444

3 or 4 h

MATH 242 or 243; 347 or 348

Elementary Real Analysis

MATH 447

3 or 4 h

MATH 242 or 243; 347 or 348

Real Variables

MATH 412

3 or 4 h

MATH 347 or 348 or CS 273

Graph Theory

MATH 413

3 or 4 h

MATH 347 or 348

Intro to Combinatorics

MATH 432

3 or 4 h

MATH 347 or 348

Set Theory and Topology
MATH 490 1 to 4 h MATH 461 or Stat 410 and one of CS 101 or 124 or equivalent Math of Machine Learning 

TAM 541

4 h

MATH 241, 285 or 380

Math Method I

TAM 542

4 h

TAM 541

Math Method II

ME 462

4 h

ME 460 and MATH 415 or consent 

Advanced Computer Control

ME 520

4 h

ME 420

Heat Conduction

ME 521

4 h

ME 411

Convective Heat Transfer

IB 494

4 h

MATH 220 or MATH 221

Theoretical Biology + Models

Other courses may be considered with pre-approval by the DGS

Table 2. List of ‘Math’ Courses above Differential equations for M.S. credit in addition to those in Table 1.

Course

Credits

Prerequisite

Title
MATH 416 3 or 4 h

MATH 241 or consent
of instructor 

 Abstract Linear Algebra 

MATH 490

3 h

Consent of instructor

Advanced Topic in Math

ME 471 or AE 420 or
CSE 451

3 or 4 h

AE 470 or CS101 and
ME370 or consent

Intro to Finite Element Analysis

ChBE 521

3 or 4 h

Consent of instructor

Applied Math in ChBE

ECE 515

4 h

ECE 486 or consent

Control System Theory & Design

ECE 517

4 h

ECE 515

Nonlinear & Adaptive Control

PHYS 508

4 h

MATH 285

Mathematical Physics I

Other courses may be considered with pre-approval by the DGS

NRES 515, Math 415, and Math 463 is not allowed for math credit effective Fall 2005.

 

Statistical design and analyses

Our handbook states that at least one course in statistical design and analyses must be taken by M.S. students and by Ph.D. students if not previously taken. The statistics course requirement cannot be taken credit/ no credit and a grade of C and above is required. A possible list of courses is listed in Table 3.

Table 3. Possible Courses in Experimental Design and Statistical Analysis

Course

Credits

Prerequisite

Title

ABE 445

4 h

CPSC 440 or Math 363

Statistical Methods
ABE 498 3 h   Applied Data Science in ABE

STAT 410 / MATH 464

3 or 4 h

Stat 400

Statistics and Probability II

STAT 420 / MATH 469

3 or 4 h

Stat 400 or 408

Methods of Applied Statistics

STAT 424 / MATH 365

3 or 4 h

Stat 410 and Math 415

Analysis of Variance

STAT 425

3 or 4 h

Stat 410

Applied Regression and Design

STAT 429

3 or 4 h

Stat 410

Time Series Analysis

STAT 458 / ANSC448

3 h

 

Math Modeling in Life Sciences

STAT 571

4 h

Stat 410 and Math 415

Multivariate Analysis

STAT 530

4 h

Math 225, 241, 242, 461

Bioinformatics

CEE 491

3 h

CEE 202

Decision and Risk Analysis

STAT 542

4 h

STAT 410 and STAT 425

Statistical Learning
CPSC 540 4 h CPSC 440 or equivalent  Applied Statistics Methods II

CPSC 543

4 h

CPSC 440

Appl. Multivariate Statistics

STAT 448 / CSE 448

4 h

STAT 400 or STAT 409 or concurrent STAT 410

Advanced Data Analysis

Other courses may be considered with pre-approval by the DGS

Undergrad ABE 440, Stat 400 and Math 463 are not allowed for statistics credit effective with Fall 2005 class. The 400 level courses would still carry graduate credit.

For MS students in TSM and PSM programs, in addition to the list in Table 3, you can take other possible statistics courses listed in Table 4. 

Table 4. Possible Courses in Experimental Design and Statistical Analysis for MS in TSM and PSM

Course

Credits

Prerequisite

Title

ABE 440 (CPSC 440)

4 h

Math 112

Applied Statistical Methods I

ACE 562

2 h

MATH 220, MATH 221, or MATH 234

Applied Regressions Models I

ACE 564

2 h

ACE 562

Applied Regressions Models II

CPSC 541

4 h

CPSC 440

Regression Analysis

NRES 502

4 h

One upper division course
recommended

Research Methods in NRES

BADM 531

4 h

 

Survey Methods in Market Research

EPSY 403

3 or 4 h

EPSY 280 or EPSY 480 or
PSYC 235 or PSYC 301

Research Methods in Learning Science

EPSY 480

4 h

 

Educational Statistics

PSYC 581         

 4h

EPSY 580

Applied Regression Analysis

PSYC 587

4 h

EPSY 581 and EPSY 582 or
SYC 406 and PSYC 407

Hierarchical Linear Models

Other courses may be considered with preapproval by the DGS

Instrumentation and Measurement

Our handbook states that at least one course in instrumentation and measurement must be taken by M.S. students and by Ph.D. students if not previously taken. The instrumentation course requirement cannot be taken credit/no credit and a grade of C and above is required. A possible list of courses is listed in Table 5. 

Table 5. Possible Courses in Instrumentation and Measurement

Course

Credits

Prerequisite

Title

ABE 425

4 h

ECE 205

Eng. Measurement Systems

ME 461

3 or 4 h

ME 360 or ABE 425

Design and Analysis of Biol. Experiments
MSE 498 3 h   Modern Methods in Material Characterization

CHEM 420 and 440

4 or 5 h

Must take both

Instrumental Characterization

ECE 414*

3 h

ECE 205 or 210 or consent

Biomedical Instrumentation

CEE 458

4 h

CEE 350

Water Resources Field Methods
BIOE 571 4 h   Biological Measurement I
BIOE 572 4 h  BIOE 571 Biological Measurement II

Other courses may be considered with pre-approval by the DGS

It is required to take both courses (ECE 414 and ECE 415) to fulfill this requirement

500 Level Courses

Our handbook states that a student must take "One 500-level course (taken for at least 3 credit hours) in an area of specialization - chosen in consultation with advisor (normally not a Special Problem; no Independent Study is allowed)."