Department Information
The Department of Mathematics and Statistics offers programs towards the Master of Science (M.S.) and Doctor of Philosophy in the Mathematical Sciences (Ph.D.) degrees. We also offer an Accelerated Master's Program (A.M.P.) which combines studies towards the Bachelor of Science (B.S.) and Master of Science degrees into one 5year program. The Statistics Program also offers Master's Degrees in Biostatistics and Statistics. This handbook does not cover those degrees. Statistics students should contact Professor Jeff Buzas. The curriculum for all degrees has two main streams: pure mathematics and applied mathematics, and in addition our PhD offeres a stream in Statistics. Students are encouraged to take courses common to both areas, enabling them to gain an appreciation of the problems and techniques in each, an understanding of the connections between them, and the common role of scientific computation.
The department is located in Innovation Hall on Central Campus. Professor Jeff Buzas is the Department Chair, as well as the Director of the Statistics Program and the Director of the Graduate Program in Statistics. Professor Gregory Warrington is the Associate Department Chair. Professor Jonathan Sands is Director of the Mathematics Graduate Program.
There is also a standing departmental Mathematics Graduate Committee in charge of overseeing the Mathematics Graduate Program. Any of the people listed above are more than willing to answer your questions about the department.
Some relevant email addresses are:
 Jeff Buzas Jeff.Buzas@uvm.edu
 Greg Warrington Greg.Warrington@uvm.edu
 Jonathan Sands Jonathan.Sands@uvm.edu
We want to help. Students with a question or a problem are encouraged to ask someone for help. Kiki Reno, our department administrator, is always a good person to start with. New graduate students will need to see Kiki for their office keys and for course materials including a textbook if they are teaching a course. She can also point out where the mailboxes and graduate student offices are located. She is available to take phone messages and in general helps keep the department running smoothly.
Networked computers are available in the graduate student offices and at many locations around campus. All graduate students have accounts on the UVM network. These accounts are maintained by the University's Information Technology department. They provide access to the Internet and a variety of installed software. Students are encouraged to acquaint themselves with the system and its capabilities. For example, one can access computer algebra systems (Mathematica, MATLAB), packages for mathematical computing (PARI, SAGE, MATLAB), mathematical typesetting software (LaTeX), and various tools for doing mathematical research.
Each graduate student receives both a postal and an electronic mailbox which are used for Department, College, and University communications.
The department sponsors several social events of note. On a weekend close to Labor Day we have our annual fall picnic. This gathering of department members, friends, and family features a potluck picnic, outdoor games, and just plain lazing in the sun (or rain, depending).
We hit the trails for a local dayhike and “leafpeeping” sometime around the end of September or beginning of October. Mathematics and statistics undergraduate students, graduate students, and faculty members have formed volleyball, basketball, and softball teams. Students traditionally challenge the faculty to a spring softball game.
The University of Vermont (UVM) is located in Burlington, a city of about 50,000 located on the shores of Lake Champlain, with views of Vermont's Green Mountains to the east and New York's Adirondack Mountains to the west. Burlington is 90 miles south of Montreal, 200 miles northwest of Boston and 300 miles north of New York City. This location affords many intellectual, cultural and recreational opportunities. Popular outdoor activities include hiking, climbing, bicycling, inline skating, mountain biking, sailing, skateboarding, skiing, and snowboarding.
Graduate Faculty and Students
The mathematics faculty at UVM has strengths in several areas (see below), and members are actively involved in research in their areas of expertise. The department and university have a friendly, collegial atmosphere in which students have ample opportunity to talk with professors on an informal basis. We strive to offer an education that is comprehensive, yet tailored to the individual needs of our students. Students are invited to participate in the ongoing research seminars in number theory, applied mathematics, complex systems, and combinatorics. These include participants from St. Michael’s College, Middlebury, and other institutions.
A list of all mathematics and statistics faculty members is available from the department's home page. There you will also find links to personal web pages of individual faculty members and graduate students. These web pages contain much more information about the individuals including, in many cases, complete copies of their recent publications.
Spencer Bachman 
Combinatorics, Algebra, Geometry 
Daniel E. Bentil 
Applied Mathematics, Modeling, Biomathematics 
Chris Danforth 
Chaos, Mathematical Modeling, Complex Systems, Computational Social Science 
Peter Sheridan Dodds 
Applied Mathematics, Complex Systems & Networks, Contagion, Computational Social Science 
Taylor Dupuy 
Number Theory, Algebraic Geometry, Model Theory 
Taras L. Lakoba 
Applied Mathematics, Modeling 
Puck Rombach 
Graph 
Jonathan W. Sands 
Algebraic Number Theory, Algebraic Graph Theory 
Christelle Vincent 
Number Theory, Geometry 
Gregory Warrington 
Algebraic Combinatorics 
J. Michael Wilson 
Fourier Analysis 
Jianke Yang 
Applied Mathematics, Nonlinear PDEs 
Jun Yu 
Applied Mathematics, Modeling, Biomathematics 


In addition to the mathematics faculty listed above, students may draw upon the statistics faculty: Jeff Buzas, Peter Callas, Paramita Saha Chaudhuri, Bernard (Chip) Cole, Abigail Crocker, and Rich Single.
Faculty with secondary appointments in our department are: Alan Ling (Computer Science: Combinatorial Design Theory and Coding Theory), and George Pinder (Civil and Environmental Engineering: Groundwater Behavior, Numerical Modeling).
University Information
The Department of Mathematics and Statistics (6562940) is situated administratively in the College of Engineering and Mathematical Sciences (CEMS). College offices are located in Votey Hall. The Dean is Dr. Linda Schadler. The Assistant Dean is Doug Dickey. The administrative assistant is Sharon Sylvester. Samantha Williams in the CEMS Student Services Office is the coordinator of services for graduate students in the college.
The Graduate College (6563160), located in the Waterman building, coordinates graduate studies across the University. Cindy Forehand is the Dean of this College. Its staff can help resolve difficulties that cannot be managed within our department. Administrative Assistants Kimberly Hess and Sean Milnamow oversee admissions and all programrelated functions for the College of Engineering and Mathematical Sciences. Along with the Mathematics Graduate Director, they will be able to help you with such things as the format of theses and dissertations, your progress through the program requirements as you near graduation, changes in your program, transfer credits, and academic standing. We encourage you to familiarize yourself with their timeline for graduation.
CEMS hosts a Graduate Student Orientation the week prior to the beginning of the fall semester. The Graduate College sponsors a Graduate Teaching Assistant of the year competition. They administer Summer Research Fellowships, and Travel MiniGrants for students to present research at professional meetings.
The Center for Teaching and Learning offers workshops on various aspects of teaching, grading and the Blackboard course management software used at UVM. These are open to teaching assistants.
A majority of our students and about half of our undergraduate majors are from the College of Arts and Sciences (CAS), also located in Waterman.
The Office of International Educational Services (6564296) coordinates programs, events, and services of special interest to international students. This includes Language and Writing Workshops, assistance in academic and cultural adjustment, and immigration and employment help. Much valuable information can be found online at http://www.uvm.edu/oie/. They also assist applicants and new students with obtaining I20 forms, Statements of Support, etc.
The Graduate Catalogue contains a wealth of essential information, such as degree requirements, university policies, and student rights and responsibilities. The latter includes academic honesty, sexual harassment, and grievance procedures. The catalog is linked to the Graduate College website at http://www.uvm.edu/~gradcoll. For another view of departmental services and of our undergraduate programs, you may wish to consult a copy of our Handbook for Majors, which can be found on the department website.
Information for Applicants
The Application Process
A student interested in graduate studies in mathematics should contact the Director of the Mathematics Graduate Program, Jonathan Sands; his email address is Jonathan.Sands@uvm.edu, and his office phone is (802) 6564339. Students interested in statistics should contact the Director of the Statistics Graduate Program, Jeff Buzas; his email address is jeff.buzas@uvm.edu. Complete applications include a statement of purpose, three reference letters, and college transcripts. International students whose native language is not English must take the Test of English as a Foreign Language (TOEFL) exam. A TOEFL score of 90 is required for admission and 100 for Teaching Assistantship consideration. Students are welcome to submit GRE scores, but they are not required.
Application forms may be found on the Graduate College's web pages at http://www.uvm.edu/~gradcoll.
Students with prior graduate study may, with the permission of the Graduate College, transfer up to 9 hours of credit from another university, or apply up to 9 hours of UVM graduate credit towards the degree. In addition, the student may apply up to 6 extra hours for courses taken during the semester in which we act upon their application.
Decisions on admissions to both the Masters and Ph.D. Programs are made on a continuing basis; students may be admitted for the fall semester during the summer just preceding it. Students may also apply to be admitted at the beginning of a spring semester. (See the subsequent section on Funding for Graduate Students for information about applying for GTA support.)
Applying to the Ph.D. Program
Well qualified applicants with a Bachelor’s degree may be admitted directly into the Ph.D. program. Students may also apply directly to the Master's program. We strongly suggest that applicants to the Ph.D. program contact potential thesis advisors in the department during the application process well before admissions decisions are made. (See also the subsequent section on Concurrent Credit for M.S. and Ph.D. programs.)
Protocol for Review of Current M.S. Students Who Apply to the Ph.D. Program
Students currently enrolled in the M.S. program who wish to apply for admission to the Ph.D. program must communicate this in writing to the Director of the Mathematics Graduate Program by January 15 of their final year in the M.S. program.
The letter of application must contain the names of at least three faculty members who may be contacted for letters of recommendation. These three references should include an individual who potentially may serve as the student's doctoral advisor. (The faculty members need not all be associated with UVM.)
The application letter must also contain a preliminary plan and timetable for completing the Ph.D., including a possible research area, a potential thesis advisor, and coursework (including reading courses) that might be needed to carry out the plan.
Admission criteria will include: the quality and level of graduate work done at UVM or other institutions, fit of the applicant's research plan within the areas of expertise of the UVM faculty, prognosis for completion of the applicant's plan, consideration of funding for the student (where applicable) during his/her doctoral studies, and comparisons with the pool of external applicants (who are also subject to these criteria). Students accepted to the doctoral program do not need to retake any UVM Mathematics qualifying exams they have already passed at the PhD level; however, they must pass both written qualifying exams at the PhD level prior to January of their 2nd year in the PhD program. Ideally, current MS students looking to transition into the PhD program will have passed the qualifying exams at the PhD level during their time in the MS program, but that is not a requirement.
Applications will be reviewed by the Mathematics Graduate Committee and applicants will be informed of their status by no later than April 15 following their application.
Funding for Graduate Students
The Mathematics Graduate Program currently has 15 Graduate Teaching Assistantships (GTAs), and, on average, about 5 of these become available to new incoming fulltime students each year. The stipend for an M.S. GTA is approximately $18,000 and for a Ph.D. GTA the stipend is $21,000. These assistantships also carry a scholarship covering up to 9 credit hours of graduate credit per semester during the period of the award.
Graduate Teaching Assistantships are usually awarded (and renewed) for an academic year beginning with the fall semester. Applicants whose files are complete by January 15 will receive full consideration for a GTA beginning the next fall semester. Applications that become complete after January 15 will be evaluated on a continuing basis for GTA support until all assistantships have been awarded.
Duties of a Teaching Assistant usually involve teaching one section of an elementary mathematics course per semester (normally 3 teaching hours per week), and conducting help sessions (1 to 2 hours per week).
Occasionally, one or more GTA positions will become available at the start of the spring semester. In such a case, all unsupported students currently in the Mathematics Graduate Program, as well as applicants who have indicated they wish to begin that spring, will be considered for the award(s).
Master’s students serving as GTAs in their first year, will normally be renewed for a second year based on good performance as a teacher and a student. Doctoral students are often supported on faculty research grants, although they are occasionally funded for one or two years by a GTA position to provide them with important teaching experience. The net amount of support from a research grant is usually comparable to that of a GTA.
GTAs are open to all applicants, domestic and international.
Fees for Graduate Students
Graduate students are responsible for certain fees (that are not covered by a GTA stipend). These include a Student Health Fee and Student Accident and Sickness Insurance. Complete information about all fees may be obtained from the Graduate College's web pages.
Information for New Students
The college runs an orientation for new students during the week before fall classes begin. Students are strongly encouraged to attend, and new recipients of Graduate Teaching Assistantships are required to attend.
During the orientation, you will learn your way around the department and meet the faculty, staff, and other graduate students. You will be given office assignments, computer accounts, and Student Identification cards. We will help you to register for your courses and give you an introduction to the University computing system, including how to use UVM email.
Graduate Teaching Assistants (GTAs) will find out their teaching assignments by the week before classes at the latest, with the goal of scheduling the assignment to accommodate courses the GTAs will enroll in themselves. Course syllabi and teaching methodology will be discussed during orientation. Generally, GTAs teach one course per semester. GTAs control their own course, planning and giving lectures, designing and grading examinations, and assigning the final grade. In some multisection courses, there are common syllabi and exams, supervised by a course leader. Usually, GTAs teach an entrylevel course. Typical topics involve elementary algebra, trigonometry, finite mathematics, and an introduction to calculus. We provide a sample syllabus and a textbook. GTAs must attend our teachertraining sessions during orientation. These sessions include delivering practice lectures and discussing common issues that arise.
We may have one to two classes available each summer for graduate students to teach. There is no guarantee that these classes will have sufficient enrollment to be offered, and cancellations can occur. Department faculty receive first priority for teaching assignments during the summer, followed by Ph.D. students doing research towards their dissertation. We generally do not offer graduatelevel classes in the summer.
GTAs should be aware that the first paycheck does not arrive until the middle of September (the last paycheck is at the end of May, after the end of spring semester). Students may request an advance on their salary under extreme circumstances. For more information please see Barbara Asiimwe in the Math/Stat office.
Graduate Student Responsibilities
As Students
Graduate students are strongly encouraged to attend the department colloquia. Invited speakers make presentations on topics of current interest in mathematics, and often provide introductory talks especially suited for graduate students. Watch for the announcements! Also, students are encouraged to participate in seminars in various research areas. Graduate students should attend the department meeting at the beginning of each semester so that they can be introduced to the rest of the department.
The University community will not tolerate discrimination or sexual harassment. Both the Graduate Catalogue and the Cat's Tale contain information about these policies. Any student who has a complaint should notify the Office of Affirmative Action, Equal Opportunity, and Diversity Programs. Likewise, academic dishonesty will not be tolerated, and policies are described in the same sources.
As Teachers
Attend the training sessions offered by the department for firsttime GTAs, and perform your teaching duties responsibly. In particular, hand out a syllabus the first day of classes detailing what material will be covered, the exam schedule, and the criteria for grading. A special rule here at UVM: you may not schedule an exam during the last 5 days of classes, so plan ahead. A very helpful listing of standard teaching practices at UVM is available on the department website.
TAs should not cancel classes without first making every effort to find a substitute teacher. The Graduate Director should be notified of all substitute teaching and any required class cancellations. TAs need to hold regularly scheduled office hours, a total of at least 3 hours per week, and inform the department of when they are (forms for this purpose are distributed at the beginning of each term). These hours should also be posted on course websites and on office doors.
Graduate Teaching Assistants are required to staff the Help Sessions. This responsibility is just as important as classroom teaching. Any TA who cannot be present must find a substitute. TAs should encourage their students to visit the Help Sessions for help outside of classes.
The graduate director, the course coordinators, and other mentors will be closely monitoring and evaluating TAs’ teaching effectiveness. They are available to help TAs if any questions related to teaching should arise.
Department and University Awards
There are several awards programs in the department and university for graduate students.
Each year at Honors Day, the department gives the John F. Kenney Award to the outstanding graduate student in mathematics. The department also gives the Sang Kil Nam Scholarship Award to an outstanding undergraduate or graduate student in mathematics and statistics.
The department has one free oneyear membership in the Mathematical Association of America and several oneyear memberships in the American Mathematical Society. The Director of the Mathematics Graduate Program awards these to the most qualified eligible graduate students. Priority goes to secondyear M.S. students. No student will receive the award more than once.
Each year, the Graduate College holds a universitywide competition for the Graduate Teaching Assistant of the Year. The department may nominate up to three students for this award (two in mathematics, one in statistics).
Each summer, the Graduate College offers a number of Graduate Student Summer Research Fellowships. For details, please contact the Graduate College.
Each fall, the university initiates a StudentFaculty Research Program. Students (junior/senior undergraduates and first year graduates) may apply for funding of a joint research project with a faculty member. Funding  including a stipend and supplies  is given for either the spring or summer. Application to this program is competitive and requires submission of a proposal for review by a faculty panel. More information, including deadlines, application forms, etc.is available from UVM's Office of Sponsored Programs (on their website as well).
Each April, the Graduate College and other offices on campus host the UVM Student Research Conference. Students engaged in research projects are strongly encouraged to participate.
Master's and Doctoral Degree Requirements
The following description of our degree requirements is included for your information. The online Graduate Catalogue contains the formal requirements including rules and regulations, e.g. those concerning residency for instate tuition.
Doctor of Philosophy in the Mathematical Sciences
The Ph.D. degree requires 75 semester hours in coursework and dissertation research. Students must maintain a 3.0 GPA. Students are required to pass two written qualifying exams and an oral examination; for details see the subsequent section on Doctor of Philosophy Qualifying Exams. Following successful completion of these exams, the student is admitted to candidacy for the degree. The candidate must then write a doctoral dissertation and pass a final oral defense of that dissertation. At least one semester of college teaching experience is required.
Master of Science Degree Requirements
Each student must complete one of the following options:
A. Twentyfour hours of acceptable graduate credits in advanced mathematics courses; six hours of thesis research culminating in a master's thesis, or
B. Thirty semester hours of acceptable graduate credits in advanced mathematics courses; no thesis required.
Under either option, students must take, or acquire the knowledge of the content in, the courses Math 331 and 333, and must satisfactorily complete at least four 300level mathematics courses. In both options, students must select a major concentration from among the areas: Analysis, Algebra, Applied Mathematics, or Discrete Mathematics. The concentration shall consist of at least nine approved credit hours in advanced mathematics courses in the respective area, three of which must be at the 300level; students in option a. may count the six hours of thesis credit towards these nine hours. (More detailed descriptions of the major concentration areas appear in the next section of this Handbook.) With approval from the student's advisor, up to six credit hours of courses outside mathematics may be used to fulfill the major or degree requirements.
To complete the MS degree, students must maintain a 3.0 cumulative GPA. They must also pass a written comprehensive exam and either pass a second written comprehensive exam or successfully defend a thesis.These exams are offered in January and August, details can be found later in the handbook. This program usually requires two years (fulltime) to complete.
Concurrent Credit for M.S. and Ph.D. Programs
Up to 30 credit hours of course work for which graduate credit is earned at UVM in a Master's degree program, whether a Master's degree is received or not, may be applied toward a Ph.D. at UVM, provided they are appropriate for the Ph.D. program. A student may thus potentially complete both the M.S. and Ph.D. degrees with a minimum of 75 credit hours of course and thesis work.
Note that an M.S. in Statistics or Biostatistics can be earned with the Ph.D. in Mathematical Science. Advising for the M.S. would be arranged through Professor Jeff Buzas in close coordination with the student’s Ph.D. advisor.
Major Concentration Areas for the M.S. Degree Program
Each of the four major concentration areas is described, and preapproved course sequences together with nonthesis sample programs are listed in each area. Note that alternate courses may be used to fulfill the Major Concentration requirement, with the approval of the student's advisor.
Major Concentration in Algebra
Students with a major concentration in Algebra must acquire a solid foundation in the fundamentals of abstract algebra, as covered in Math 251 and Math 252. Students are expected to go beyond this foundation by achieving some degree of depth in a more specialized area, as is normally treated in Math 351, Topics in Algebra.
Students wishing to pursue a major concentration in applied (or numerical) algebra may do so under the rubric of Applied Mathematics.
Preapproved Course Requirements: the knowledge of Math 251, 252, and either 351 or an approved 395 course in algebra.
Sample Program with a Major Concentration in Algebra
 First Year, fall: Math 241 & 251 & XXX (9 cr hrs)
 First Year, spring: Math 242 & 252 & 331 (9 cr hrs)
 Second Year, fall: Math 333 & 351 (6 cr hrs)
 Second Year, spring: Math XXX & XXX (6 cr hrs)
A third course in the fall of the Second Year could be taken in place of, or in addition to, the Third course in the fall of the First Year.
Major Concentration in Analysis
The student must acquire a firm knowledge of undergraduate analysis (Math 241 and 242), the basics of measure theory and the Lebesgue integral (Math 333), and the theory of functions of a complex variable (Math 331). The student should then build on this foundation to acquire a deeper knowledge and expertise in a more specialized area, such as functional analysis (Math 335, Math 336), differential equations (Math 330, Math 339), or Fourier analysis (Math 395: Special topics).
Preapproved Course Requirements: the knowledge of Math 335, plus 330 or 339, and either 240 or an approved 395 course in analysis.
Sample Program with a Major Concentration in Analysis
 First Year, fall: Math 241 & 251 & one of 237,330,332,339 (9 cr hrs)
 First Year, spring: Math 242 & 252& 331 (9 cr hrs)
 Second Year, fall: Math 333 & one of 332,330,339 (6 cr hrs)
 Second Year, spring: Math 335,& XXX (6 cr hrs)
Major Concentration in Applied Mathematics
In tandem with taking the common core of courses in real and complex analysis, students with a major concentration in Applied Mathematics must acquire a rigorous knowledge of foundational areas of applied mathematics such as numerical analysis (Math 237, 337) and differential equations (Math 330, 339). In addition, students must gain an indepth experience involving the application of mathematics to realworld problems; such experience may be obtained through mathematics courses (eg., Math 266, Math 268, Math 300, Math 303), statistics courses (e.g. Stat 287, Stat 387), or through appropriate graduatelevel courses outside the Mathematics Program. In the latter case, students must obtain prior written approval from their advisor to ensure that the courses outside the Mathematics Program appropriately supplement the applied mathematics courses to form a cohesive, indepth major concentration.
Alternatively, students may pursue an applied mathematics concentration with a focus in appropriate areas of algebra, analysis, or discrete mathematics. In such areas, the mathematical foundation component may be different (eg., Math 274 may be appropriate), and again courses outside the Mathematics Program may be utilized or even required (in CS, for example).
However, the fundamental stricture – that students must gain a solid mathematical foundation coupled with significant indepth experiences in an applications area – must be met through a carefully advised, preapproved program.
PreApproved Course Requirements: the knowledge of Math 237 and 268, and either 330 or 339.
Sample Program with a Major Concentration in Applied Math
 First Year, fall: Math 237 & 241 & 330 (9 cr hours)
 First Year, spring: Math 337 & 242 & 331 (9 cr hrs)
 Second Year, fall: Math 333 & 268 (6 cr hours)
 Second Year, spring: Math 339 & 266 (6 cr hrs)
Major Concentration in Discrete Mathematics
Students with a major concentration in Discrete Mathematics must acquire a solid foundation in the fundamentals of abstract algebra, as covered in Math 251 and they must also acquire a working knowledge of graph theory (Math 273). Further, students are expected to go beyond this foundation by achieving some degree of depth in a more specialized area, as is normally treated in Math 373, Topics in Combinatorics.
Preapproved Course Requirements: the knowledge of Math 251 and 273, and either 373 or an approved 395 course in combinatorics or related area of theoretical computer science.
Sample Program with a Major Concentration in Discrete Math
 First Year, fall: Math 241 & 251 (6 cr hrs)
 First Year, spring: Math 242 & 252 & 273 (9 cr hrs)
 Second Year, fall: Math 333 & 3xx & 373 (9 cr hrs)
 Second Year, spring: Math 331 & 2xx (6 cr hrs)
It is essential that students interested in pursuing the major concentration in Discrete Mathematics discuss their plan of study with an advisor as there are many possibilities for the program.
Written Examinations
The nature and timing of the written and comprehensive examinations depend on your degree objective and whether you've concentrated on pure or applied mathematics.
Master's Comprehensive Examination
All students in the nonthesis version of the M.S.degree are required to pass 2 written comprehensive examinations. These written exams are given during the week prior to the beginning of the semester in August and January. MS students must pass both exams by January of the student's second year. Two attempts for each exam are allowed.
Any student planning on taking a written examination should contact the Director of the Mathematics Graduate Program at the end of the prior semester. In consultation with the Graduate Committee, the Director will appoint a committee to author the written examination. The exam generally requires three hours.
Students completing the thesis option of the MS will be examined in their major concentration as part of their (required) thesis defense; therefore they only need to pass a single written comprehensive exam.
The guidelines for the M.S. written examination only require that the student has knowledge of the material; it does not require that the student has taken the corresponding courses. Thus, a student who has entered the program with a strong background may not need to take the courses listed above.
Specific examination topics are listed below, as the exams are the same as those offered to PhD students, but with a different passing criteria. All MS students must pass the comprehensive examination in analysis. The second exam can be in a topic of the student's choosing.
Doctor of Philosophy Examinations
Doctoral candidates are required to pass two qualifying exams and a comprehensive exam.
Students will take written qualifying examinations in a total of two areas (in no particular order). Students with a concentration in Pure Mathematics will take exams in:
 1. Real & Complex Analysis (331 and 333)
 2. Algebra (251 and 252)
Students with a concentration in Applied Mathematics will take exams in:
 1. Numerical Analysis (237 and 337)
 2. Differential Equations (330 and 339)
In addition, all students will take a comprehensive exam consisting of a written and oreal presentation on a topic chosen in consultation with their research studies committee, whose members will be present for the oral examination. Topics currently offered include
 3a. Number Theory (255 and 351)
 3b. Combinatorics (273 and 373)
 3c. Functional Analysis (335)
 3d. Methods of Applied Mathematics (395)
 3e. Complex Systems (266 and 300)
 3f. Nonlinear PDEs (395)
 3g. Mathematical Biology and Ecology (268)
Students interested in taking an exam on a topic not found in the above list are encouraged to contact the Graduate Director regarding other possible exam topics.
These exams are offered twice a year: at the start of the spring semester (the middle of January), and immediately before the academic year begins (the end of August).
Students are encouraged to try their first qualifying exam by the end of the first year of study. They must pass both qualifying exams at the PhD level by January of their second year of study, and pass all three exams by August of the third year.
One to two months in advance of each exam date, the mathematics graduate director will send out an email reminder, which will give a deadline for students to inform the director of their intention to take an exam. The Graduate Committee will prepare, administer, and grade the exams. Students can expect to hear the results two weeks after taking the exam.
Doctoral students taking a qualifying exam are encouraged to get a syllabus from the Director of the Mathematics Graduate Program. Separate syllabi are available in each area. Some previous exams with solution sets are posted on the “Graduate Programs” section of the departmental website.
Master's Degrees and Advancement to Candidacy for Doctoral Students
After successful completion of the qualifying exams, a doctoral candidate can petition for awarding of the M.S. degree and advance to candidacy for the Ph.D. degree. A student may be awarded the M.S. degree but not be admitted to candidacy for the Ph.D. if the student fails to pass a particular examination at the PhD level, but is deemed to have done sufficiently well in their coursework and examinations.
Advising and Program Requirements
All graduate students should periodically check the Graduate College’s web pages for Current Students (http://www.uvm.edu/~gradcoll) for relevant information. The director of the Mathematics Graduate Program will pair each student with an advisor from among the Graduate Faculty. Students are welcome to change advisors in accord with their interests.
Because of the size of the graduate program and the need to provide funding for students, applicants to the Ph.D. program are generally not admitted without their first identifying a suitable research and dissertation advisor (or advisors). As soon as possible after matriculation, the student should, in consultation with the Graduate Program Director and identified faculty advisor, establish a Studies Committee. It is the responsibility of the Studies Committee to supervise the graduate student’s program and to review progress at regular intervals. The Studies Committee consisting of at least three members of the Graduate Faculty is appointed by the Graduate Program Director and approved by the Dean of the Graduate College. The student’s academic/dissertation advisor serves as the Chairperson of the Studies Committee. On occasion, it may be appropriate for a professional other than a member of the Graduate Faculty to serve as a member of the Studies Committee, and in this case, written approval must be obtained from the Dean of the Graduate College prior to the student’s beginning dissertation research.
The Studies Committee must meet with the student at least annually, and more frequent consultation is both highly recommended and encouraged. A brief report from these meetings certifying appropriate progress toward completion of the degree (e.g., timely completion of required Qualifying Exams) and indicating any items of importance (e.g., agreedupon timelines for completion of thesis work) appropriate for the record should be prepared for the Graduate Program Director. These reports will be forwarded to the Graduate College and placed in the candidate’s graduate record.
The graduate students play an important role in the shaping of our curriculum. The graduate committee welcomes suggestions on course offerings. If a number of students would like to take a particular course they should contact the Director of the Mathematics Graduate Program or a professor with interests in that area. This is especially important for topics courses in areas where students want to do thesis research.
Most 200 level courses are available for both undergraduate and graduate credit. In these courses, students attend common lectures. Usually, however, the graduate students may be given additional homework assignments and different exams and held to higher grading standards. Please note that credit for Math 230 does not apply towards a degree in Mathematics. Math 230 is considered elementary material which a student should have had as an undergraduate. A student who needs to review this material may sit in on this course without credit, or study the material independently. If they do take the course for credit the tuition will be covered under a Graduate Teaching Assistantship, but the credits earned will not count towards the degree.
Master's students choosing the thesis option may sign up for Math 391, Master's Thesis Research. Doctoral students may sign up for Math 491, Doctoral Dissertation Research. All students are recommended to take Math 330, Advanced Ordinary Differential Equations, but this course is not required.
Students and advisors should carefully read the section on qualifying and comprehensive examinations in this handbook for information on the scheduling, topics, and structure of these exams.
Advisors should note that GTA students carry a tuition waiver covering up to 9 credits per semester. A minimum of 6 credits per semester is needed to maintain fulltime graduate student status. Credits above the 9 credit remission per semester are charged to the student on a per credit basis. However, with the approval of the Graduate College and the student's advisor, it may be possible to waive this extra fee, provided that a student takes at most 24 credits per year. Students interested in this possibility should ask the Graduate College.
Depending on the degree option, a student may be required to pass a thesis defense (applicable to all doctoral candidates, and to those masters candidates writing a thesis) depending on the program of study. A student writing a thesis or dissertation should consult the Thesis and Dissertation information linked to the Graduate College webpage under “Policies and Procedures.”
On occasion, a graduate student may desire to postpone or suspend studies for a period of time. Students may apply for up to one year's leave of absence, a semester at a time. Application forms are available at the Graduate College. A student who has finished all coursework but not yet graduated can sign up for Continuous Registration. This option maintains one’s fulltime student status while minimizing tuition costs. This is done by registering for a semester of GRAD 903 at a cost of $300 per semester.
Sample Schedule for the Doctoral Program
Pure  Applied  
Fall Year 1  241, 245, & Elective  241, 237, & Elective 
Spring Year 1  242, 252, & 331  242, 339, & 331 
1st Ph.D. Written Exam  
Fall Year 2  333 & First Topic Elective  330 & First Topic Elective 
2nd Ph.D. Written Exam  
Spring Year 2  Second Topic Elective & Elective  Second Topic Elective & Elective 
3rd Ph.D. Written Exam  
Subsequent Years  Elective & 491  Elective & 491 
Accelerated Master's Program
The Accelerated Master’s Program (AMP) in mathematics is designed so that students with strong ability and motivation can complete a bachelor's degree in mathematics, science or engineering, as well as a master's degree in mathematics at UVM within five years. Interested students should also see the general description of Accelerated Master’s Programs on the Graduate College web pages.
The first four years consist of an undergraduate program that includes the core requirements for a minor in mathematics together with other courses that lay a solid mathematical foundation; this portion culminates in a bachelor's degree. During the fifth year, students take courses that complete the requirements for the master's degree in mathematics.
The AMP in mathematics integrates the undergraduate and graduate experiences so that students receive both the breadth and depth they would achieve had they completed the two degrees separately.
Requirements for Admission
The Accelerated Master’s Program in mathematics is designed for students who are mathematics majors, or who are majors in science or engineering with a minor in mathematics. A student who is enrolled in this AMP may count 6 credits of Mathematics coursework at or above the 200level for both the undergraduate and the graduate degrees. If interested in the AMP in Mathematics, a student declares this interest in writing to the Director of the Mathematics Graduate Program before taking any courses that he or she would like to count towards both degrees. The student should also be sure to inform the course instructors that he or she wishes to participate in these courses at the graduate level.
Formal application for the Accelerated Master’s Program in mathematics is made during the spring preregistration period of the student's junior year. The student carries out the usual procedure for admission to the M.S. Program in mathematics, including the submission of letters of recommendation. Application forms are available online. The student's admissions essay must specifically address why the student wishes to enter the Accelerated Master’s Program.
Applicants must achieve the following by the end of their junior year:
 Completion of Math 21, 22, 52, 121, and 124 with an overall GPA of 3.0 or higher,
 Completion of Math 241 and Math 242 with grades of B+ or better in each, and
 Completion of a least two additional 200level mathematics or statistics courses with grades of B+ or better in each.
Requirements for Advancement to Candidacy in the AMP
Students who have been admitted to the Accelerated Master’s Program in mathematics normally advance to candidacy in this program at the end of their senior year. This marks the end of their undergraduate curriculum and the beginning of their graduate curriculum. The criteria for advancement to candidacy are:
 Completion of a bachelor’s program in mathematics at UVM, or completion of a bachelor's program in science or engineering at UVM with a minor in mathematics;
 Completion of at least two additional mathematics or statistics courses at the 200level with grades of B or better in each (these are in addition to Math 241, 242 and the two 200level courses required for admission to the program); and
 Completion of a 300level course in Mathematics with a grade of B or better. This course may not be counted towards the student's undergraduate degree or GPA, and so must be taken as an overload. Note that AMP students will need to know material from the Spring course offerings of Math 331, Complex Analysis, by the time they take the comprehensive exam in January of their 5th year.
Students who have been admitted to the AMP on the completion of their junior year but who later fail to meet the requirements for advancement to candidacy for the M.S. degree will only be permitted to continue towards their M.S. degree after review by the Graduate Program Committee and with the written approval of the Director of the Graduate Program in Mathematics.
Requirements for Completion of the Master’s Degree Portion of the Program
A total of 30 credits are required for the Master’s degree, of which 6 may be in Mathematics courses at the 200level or above that were counted towards the Bachelor’s degree, and 3 or 4 may come from a 300level course taken as an overload in the undergraduate years. Fulfilling the M.S. degree requirements then calls for the completing the remaining credit hours, including two 300level mathematics courses. The course of study must include either 6 credits of thesis work or 9 credits in an approved major subject featuring a 300level course. Students must pass the M.S. written comprehensive exams covering analysis and a second option. These exams will also cover the major subject unless the student has chosen the thesis option, in which case there will be a thesis defense. For more details, see the section of the Handbook for Graduate Studies in Mathematics that is devoted to Master of Science Degree Requirements.
Sample AMP Schedule
First Year  Fall  Math 21 (4 hrs), CS 21, & 3 Electives  16 Hours 
First Year  Spring  Math 22 (4 hrs), Math 52, & 3 Electives  16 Hours 
Second Year  Fall  Math 121 (4 hrs), Math 141/151/or 1XX, & 3 Electives  16 Hours 
Second Year  Spring  Math 124, Math 1XX or 2XX, & 3 Electives  15 Hours 
Third Year  Fall  Math 241, Math 230 or 2XX, & 3 Electives  15 Hours 
Third Year  Spring  Math 242, Math 2XX, & 3 Electives  15 Hours 
Fourth Year  Fall  Math 333, Math 251, & 3 Electives  15 Hours 
Fourth Year  Spring  Math 252 or 2XX, Math 331 or 3XX, & 3 Electives  15 Hours 
Fifth Year  Fall  Math 2XX, 2XX, 3XX  9 Hours 
Fifth Year  Spring  Math 2XX or 3XX & Math 3XX & 3XX  9 Hours 
Course Descriptions
The following informal course descriptions are included for your information. The catalog contains the formal course descriptions, credit hours, and prerequisites.
Mathematics Courses
Math 230 Ordinary Differential Equations Solutions of ordinary differential equations, the Laplace transformations, series solutions of differential equations. This course is available for graduate credit for students in other areas, but credit for this course will not count towards a mathematics graduate degree.
Math 231 Real Analysis in One Variable This course is intended to be a terminal course in real analysis. It is not available for graduate credit. Graduate students should take Math 241 in its place.
Math 236 Calculus of Variations Basic theory: first and second variations; EulerLagrange equation for extremals; isoperimetric problems; Hamilton's principle. Emphasis on classical examples and applications.
Math 237 Introduction to Numerical Analysis This course develops methods for obtaining numerical solutions to problems too difficult to be solved “in closed form.” In addition, the course explores the effect of fixed precision arithmetic when using a computer with these methods. Topics include: methods for solving f(x)=0 and a discussion on efficiency of the computations; methods for solving systems of n equations with n unknowns, and the effect of having nearly duplicate information (illconditioned systems); the problem of generating a function or functions to describe a given set of data (interpolation, splines, least squares); and the problem of estimating the value of definite integrals too difficult to find antiderivatives for.
Math 240 Fourier Series and Integral Transforms The purpose of the course is to study integral operators and related Fourier series expansions which are useful in solving initial and boundary value problems for differential equations. Topics covered include Green's functions for initial and boundary value problems, Laplace transforms, classical and more general Fourier expansions, eigenvalue methods for boundary value problems.
Math 241 Real Analysis I Much of mathematics is traditionally divided into the general areas of Abstract Algebra and Analysis. In our curriculum, these topics are introduced in Math 251 and Math 241. Math 241, Real Analysis I, may be viewed as an extension of the main ideas from Calculus: Limits, derivatives, integrals, and convergence of sequences and series. In this course, the student studies the basic properties of sets of real numbers, including the topology of Euclidean nspace, and analyzes the behavior of realvalued functions defined on such sets. This sets the stage for generalizations such as abstract metric spaces and other more modern notions.
Math 242 Real Analysis II This is the spring semester continuation of Math 241. Topics normally include differentiation in Rn, RiemannStieltjes integral, uniform convergence of functions, Inverse and Implicit Function Theorems.
Math 251 Abstract Algebra I Basic theory of groups, rings, fields, homomorphisms, and isomorphisms. The course is set at the level of the text Abstract Algebra, Second Edition by D. Dummit and R. Foote (John Wiley & Sons, Inc.).
Math 252 Abstract Algebra II This is the spring semester continuation of Math 251. Topics include modules, vector spaces, linear transformations, rational and Jordan canonical forms, finite fields, field extensions, and Galois Theory leading to the insolvability of quintic equations.
Math 255 Number Theory Number theory has been called the “Queen of Mathematics” because it involves the development of a wide variety of useful methods and produces beautiful and profound results concerning one of the most basic objects in mathematics: the natural numbers. This course allows one to appreciate the work of many mathematical giants and some of the relationships between geometry, algebra, and analysis. Those interested in the history or teaching of mathematics, or in obtaining a concrete introduction to some of the concepts of abstract algebra will find this to be an attractive course which can be taken early in the major. The ideas found here, originally thought to be purely theoretical, now find applications of critical importance to society.
Math 257 Topics in Group Theory Topics may include abstract group theory, representation theory, classical groups, Lie groups.
Math 264 Vector Analysis Fundamental algebraic properties of vectors in Euclidean spaces. Applications to geometry and physics. Tensors.
Math 266 Chaos, Fractals, and Dynamical Systems Discrete and continuous dynamical systems, Julia sets, the Mandelbrot set, period doubling, renormalization, Henon map, phase plane analysis, and Lorenz equations.
Math 268 Mathematical Biology and Ecology Mathematical modeling in the life sciences. Topics include population modeling, dynamics of infectious diseases, reaction kinetics, wave phenomenon in biology, and biological pattern formation.
Math 272 Applied Analysis Fourier series; solution of the wave, heat, and Laplace equations by eigenfunction expansions; FourierBessel series; complex variables; conformal mapping; SchwarzChristoffel mappings.
Math 273 Combinatorial Graph Theory A graph is a collection of points together with lines joining pairs of these points. Graphs are used to model a wide variety of situations. For example, the points may represent microprocessors with the lines representing communication channels. When can we lay out the computer circuit without crossing wires? Or perhaps the points of a graph represent people and the lines indicate friendships. When can we pair together the people so that everyone is with a friend? Or perhaps the points are airports and the lines are flights. How can a traveler arrange his or her schedule to minimize the total cost of her trip? These and other topics are explored in Math 273, Combinatorial Graph Theory. The questions above are respectively special cases of graph planarity, matching theory, and the Hamilton cycle problem. You will also study paths and trees in graphs (connection problems), Eulerian cycles (postman problems), and point and line colorings (storage and scheduling). Finally, what does all this have to do with the five Platonic solids and the thirteen semiregular polyhedra? Take the course and find out!
Math 274 Numerical Linear Algebra The purpose of the course is to study numerical algorithms for solving systems of linear equations, manipulating matrices, solving least squares problems, determining rank computationally, finding eigenvalues, and determining sensitivity of solutions to perturbations. The course emphasizes the importance of determining numerical stability of algorithms since floating point computation inevitably introduces errors compared with ideal exact computations.
Math 275, 276 Advanced Engineering Analysis I, II Problems in analysis in engineering, including ordinary and partial differential equations, special functions, matrices, tensor analysis, variational calculus, complex variables, perturbation methods.
Math 295 Special Topics Math 295 adds variety to the schedule of course offerings by providing for the study of different topics each semester upon the initiative of students or of faculty members. A student or group of students interested in pursuing a particular topic may request to enroll in one of these courses for this purpose. They may approach a particular faculty member, or ask the chair of the department for assistance in locating a suitable instructor. Faculty members tend to be quite receptive when students come forward with ideas demonstrating a real enthusiasm for mathematics. Another possibility is that a faculty member may announce the intention to teach a Math 295 course on a topic of particular interest to him or her. Watch for these announcements in your mathematics classes or consult your advisor. These courses often involve some independent study and other activities which take advantage of their intimate and informal nature.
Math 300 Principles of Complex Systems Introduction to fundamental concepts of complex systems. Topics include emergence, scaling phenomena, and mechanisms, multiscale systems, failure, robustness, collective social phenomena, complex networks. Students from all disciplines welcomed. Pre/corequisites: Calculus and statistics required. Linear Algebra, Differential Equations, and Computer programming recommended but not required. Crosslisting: CSYS 300. Credits: 3
Math 303 Complex Networks Detailed exploration of distribution, transportation, smallworld, scalefree, social, biological, organizational networks; generative mechanisms; measurement and statistics of network properties; network dynamics; contagion processes. Students from all disciplines welcomed. Pre/corequisites: Math 300/CSYS 300, Calculus, and Statistics required. Crosslisting: CSYS 303. Credits: 3
Math 330 Advanced Ordinary Differential Equations Linear and nonlinear systems, approximate solutions, existence, uniqueness, dependence on initial conditions, stability, asymptotic behavior, singularities, selfadjoint problems.
Math 331 Theory of Functions of Complex Variables Differentiation, integration, Cauchy Riemann equations, infinite series, properties of analytic continuation, Laurent series, calculus of residues, contour integration, meromorphic functions, conformal mappings, Riemann surfaces.
Math 332 Approximation Theory Interpolation and approximation by interpolation, uniform approximation in normed linear spaces, spline functions, orthogonal polynomials. Least square, and Chebychev approximations, rational functions.
Math 333 Theory of Functions of Real Variables The theory of Lebesgue integration, Lebesgue measure, sequences of functions, absolute continuity, properties of Lp spaces.
Math 335, 336 Advanced Real Analysis L²spaces, Lpspaces; Hilbert, Banach spaces; linear functionals, linear operators; completely continuous operators (including symmetric); Fredholm alternative; HilbertSchmidt theory; unitary operators; Bochner's Theorem; FourierPlancherel, Watson transforms.
Math 337 Numerical Differential Equations Numerical solution and analysis of differential equations: initialvalue and boundaryvalue problems; finite difference and finite element methods. Prerequisites: MATH 237, either MATH 230 or 271 recommended. Credits: 3
Math 339 Partial Differential Equations Classification of equations, linear equations, first order equations, second order elliptic, parabolic, and hyperbolic equations, uniqueness, and existence of solutions.
Math 342 Computability and Recursive Function Theory Crosslisted as Computer Science 342.
Math 351 Topics in Algebra Topics include algebraic number theory, commutative algebra, modular forms, representation theory, algebraic geometry, and the arithmetic of elliptic curves. May be repeated for credit with the permission of the instructor and the advisor.
Math 353 PointSet Topology Topological spaces, closed and open sets, closure operators, separation axioms, continuity, connectedness, compactness, metrization, uniform spaces.
Math 354 Algebraic Topology Homotopy, covering spaces, homology and cohomology theories, fixed point theorems.
Math 373 Topics in Combinatorics Topics will vary each semester and may include combinatorial designs, coding theory, topological graph theory, cryptography.
Math 382 Seminar Topical discussions with assigned reading. This one credithour course is required of M.S. candidates.
Math 391 Master's Thesis Research Provides up to 6 credits for research and preparation of an M.S. Thesis.
Math 395 Special Topics See the description for Math 295. This course is now available for variable credits.
Math 491 Dissertation Research This course provides credit for the research and preparation of your Ph.D. dissertation.
Statistics Courses
Stat 200 Medical Biostatistics Introductory design and analysis of medical studies. Epidemiological concepts, casecontrol, and cohort studies, and clinical trials. Students evaluate statistical aspects of published health science studies. (Required for Biostat M.S.)
Stat 211 Statistical Methods I Fundamental concepts and techniques for data analysis and experimental design, descriptive and inferential statistics, including classical and nonparametric methods, regression, correlation, and analysis of variance. This course is taken as an introduction by graduate students of various disciplines (but would not count as credit for the statistics or biostatistics M.S. degrees).
Stat 221 Statistical Methods II Multiple regression and correlation, basic experimental design. analysis of variance (fixed, random and mixed models) and analysis of covariance. Computer software usage. (Required for the stat/biostat M.S.)
Stat 223 Applied Multivariate Analysis Analysis methods for categorical and continuous multivariate data, discriminate analysis, logistic regression, canonical correlation, principal components, factor analysis and log linear models, computer software usage. (Required for the stat/biostat M.S.)
Stat 224 Statistics for Quality and Productivity Statistical methods for product quality and productivity, statistical process controls Shewhart and CUSUM, control charts, acceptance, continuous, sequential sampling. Selected statistical computer programs utilized. (Required for the stat M.S.)
Stat 225 Applied Regression Analysis Regression analysis is concerned with how to predict the values of one variable (y) from the given values of other variables (x1, x2, …); the simplest example being a straight line (linear) relation f(x)=ax+b. With real data collected on y and x the function f never exactly explains y in terms of the xvalues, and so one typically uses a least squares error approach to obtain the best fitting functions to the data. Regression analysis is the basic method used in business, economics and other social/behavioral sciences, health and other biological sciences, and engineering and other physical sciences to fit mathematical models of real data. The standard models and assumptions are reviewed, fitting methods discussed (with software and appropriate matrix methods), and methods of checking the fit (residual analysis) are covered. Linear, multiple, polynomial, logistic, and ridge regressions are included. Important forecasting applications provide some of the examples.
Stat 231 Experimental Design Randomization, complete and incomplete blocks, crossovers, covariance analysis, factorial experiments, confounding, fractionalreplication, nesting, split plots, repeated measures, response surface optimization, Taguchi methods, and optimal designs. (Required for the stat/biostat M.S.)
Stat 233 Design of Sample Surveys Design and data analysis for sample surveys. Simple random, stratified, systematic, cluster, multistage sampling. Practical issues in planning and conducting surveys.
Stat 237 Nonparametric Statistical Methods Nonparametric and distribution free methods; categorical, ordinal and quantitative data; confidence intervals; rank and ChiSquare hypothesis tests; computerintensive procedures (Bootstrap, exact tests).
Stat 241 Statistical Inference Introduction to statistical theory: related probability fundamentals, derivation of statistical principles and methodology for parameter estimation and hypothesis testing. (Required for biostat M.S., but mathematics graduates would be recommended to take Stat 261.)
Stat 251 Probability Theory Distributions of random variables and functions of random variables. Expectations, stochastic independence, sampling and limiting distributions (central limit theorems). Concepts of random number generation. (Required for stat M.S.)
Stat 253 Applied Time Series and Forecasting A fundamental problem in business, economics, engineering, and other sciences is providing forecasts for the future values of data collected in time order. Modern statistical models (autoregressive integrated moving average models) are developed in this course, which can be used in the analysis of time series data, fitting models to these data, checking forecast errors, and providing future forecasts. Seasonal effects are considered as well. If information from other series (x) can be used to aid in the forecast of y, then one can use transfer function models for the analysis. You gain practical experience in fitting a variety of real series with the latest software methods.
Stat 261 Statistical Theory I Point and interval estimation, hypothesis testing, and decision theory. Applications to areas such as nonparametric tests, sequential analysis, and linear models. (Required for stat M.S.)
Stat 287 Data Science I Data harvesting, cleaning, and summarizing. Working with non traditional, nonnumeric data (social network, natural language textual data, etc.). Scientific visualization using static and interactive "infographics". A practical focus on real datasets, and developing good habits for rigorous and reproducible computational science.
Stat 330 Bayesian Statistics Introduction to Bayesian inference. Posterior inference, predictive distributions, prior distribution selection. MCMC algorithms. Hierarchical models. Model checking and selection. Use of computer software.
Stat 355 Statistical Pattern Recognition Analysis of algorithms used for feature selection, density estimation, and pattern classification, including Bayes classifiers, maximum likelihood, nearest neighbors, kernels, discriminants, neural networks, and clustering.
Stat 360 Linear Models Theory of linear models, least squares and maximum likelihood estimation, fixed, random and mixed models, variance component estimation, introduction to generalized linear models, bootstrapping.
Stat 387 Data Science II Advanced data analysis, collection, and filtering. Statistical modeling, monte carlo statistical methods, and in particular Bayesian data analysis, including necessary probabilistic background material. A practical focus on real datasets and developing good habits for rigorous and reproducible computational science.