MEMORANDUM
TO: Sean Rafferty, Senate Chair
FROM: Havidán Rodríguez, President
DATE: February 25th, 2021
SUBJECT: Senate Bill Approval
I am pleased to approve the following Senate Bill, which was recommended
following approval by the University Senate at its meeting of February 24th, 2021.
Senate Bill 2021-05:
PROPOSAL TO ESTABLISH AN ADVANCED CERTIFICATE IN
MACHINE LEARNING
Approved: ______________________________
Havidán Rodríguez, President
Senate Bill 2021-05
UNIVERSITY SENATE
UNIVERSITY AT ALBANY
STATE UNIVERSITY OF NEW YORK
Introduced by:
Graduate Academic Council
University Policy and Planning Council
Date: February 22, 2021
Proposal to Establish a New Advanced Certificate in Machine Learning.
IT IS HEREBY PROPOSED THAT THE FOLLOWING BE ADOPTED:
1. That the University Senate approves the attached Program proposal as submitted by the College of
Engineering and Applied Sciences, to the Graduate Academic Council and the Undergraduate Policy
and Planning Council
2. That this takes effect for the Fall 2021 semester.
3. That this proposal be forwarded to President Havidán Rodríguez for approval.
University at Albany – State University of New York
College of Arts and Sciences
Course and Program Action Form
Proposal No.
19-097
Please check one:
Course Proposal
X
Program Proposal
Please mark all that apply:
New Course
Revision of:
Number
Description
Cross-Listing
Title
Prerequisites
Shared-Resources Course
Credits
Deactivate/Activate Course (boldface & underline as appropriate)
X
Other (specify):
New Online Graduate Certificate
Program
Department:
Mathematics and Statistics
Effective Semester, Year: Summer 2020
Course Number
Current:
New:
Credits: 9
Course Title:
Online Graduate Certificate Program “Machine Learning”
Course Description to appear in Bulletin:
The Certificate in Machine Learning offers a combination of theoretical foundations and a variety of applications that bring participants to the forefront of this
fundamental area of modern Data Science. Participants will be taught necessary theoretical mathematical background including basic concepts of Functional
analysis typically used in applications, nonlinear programming and optimization methods, and the most up-to-date concepts of Machine Learning. Along with
theoretical material students acquire knowledge of contemporary Machine learning software and practical skills of working with this software. Numerous examples
coming from real life problems are included. The program is designed with courses that, when taken in sequence, allow a participant with no degree in advanced
mathematics develop enough background knowledge and skills to gain graduate level expertise in Machine Learning. The Certificate is offered completely online.
It consists of 3 courses:
AMAT 590 Function Theory and Functional Analysis for Applications (3 credits)
AMAT 591 Optimization Methods and Nonlinear Programming (3 credits)
AMAT 592 Machine Learning (3 credits)
Prerequisites statement to be appended to description in Bulletin:
A standard sequence of three calculus courses, including multivariable calculus (AMAT 112, 113, 214 at UAlbany), an undergraduate course in linear algebra (such
as AMAT 220), and a course in statistics (such as AMAT 108).
If S/U is to be designated as the only grading system in the course, check here:
This course is (will be) cross listed with (i.e., CAS ###):
This course is (will be) a shared-resources course with (i.e., CAS ###):
Explanation of proposal:
(Undergraduate Course/Program proposals: please address the effect on the department’s General Education competency plan)
Online Graduate Certificate Program "Machine Learning" is directed to both current graduate students and working professional willing to enhance their skills in
this contemporary area of Data Science. It is focused on fundamental mathematical principles of contemporary Machine Learning. Students will be taught necessary
theoretical mathematical background including basic concepts of Functional analysis typically used in applications, nonlinear programming and optimization
methods, and the most up-to-date concepts of Machine Learning. Along with theoretical material students acquire knowledge of contemporary Machine learning
software and practical skills of working with this software. Numerous examples coming from real life problems are included.
Other departments or schools which offer similar or related courses and which have certified that this proposal does not overlap their offering:
This is the first completely online program in Machine Learning at Albany.
If this proposal is for an interdisciplinary program, please indicate the Department where the major/minor will be housed:
Chair of Proposing Department (TYPE NAME)
Administrative Manager or Department Secretary (TYPE NAME)
Date
Michael Stessin
Joan Mainwaring
09/30/19
Approved by Chair(s) of Departments having cross-listed course(s) [Copy of e-mail
approval(s) on following page.]
Date
Dean of College
Date
Caren Stark
2/18/20
Chair of Academic Programs Committee
Date
Dean of Undergraduate or Graduate Studies
Date
Alejandra Bronfman
11/6/19
Form: CAS Course and Program Action Form (revised 10/19/16)
New Program Proposal:
Certificate or Advanced Certificate Program
Form 2C
Version 2016-10-13
This form should be used to seek SUNY’s approval and New York State Education Department’s (SED) registration of a
proposed new academic program leading to a certificate or an advanced certificate. Approval and registration are both
required before a proposed program can be promoted or advertised, or can enroll students. The campus Chief Executive or
Chief Academic Officer should send a signed cover letter and this completed form (unless a different form applies1), which
should include appended items that may be required for Sections 1 through 5 and 10 of this form, to the SUNY Provost at
program.review@suny.edu. The completed form and appended items should be sent as a single, continuously paginated
document.2 If Sections 7 and 8 of this form apply, External Evaluation Reports and a single Institutional Response should
also be sent, but in a separate electronic document. Guidance on academic program planning is available here.
Table of Contents
NOTE: Please update this Table of Contents automatically after the form has been completed. To do this, put the
cursor anywhere over the Table of Contents, right click, and, on the pop-up menus, select “Update Field” and then
“Update Page Numbers Only.” The last item in the Table of Contents is the List of Appended and/or Accompanying Items,
but the actual appended items should continue the pagination.
Section 1. General Information ............................................................................................................................................... 4
Section 2. Program Information .............................................................................................................................................. 5
2.1. Program Format .................................................................................................................................................. 5
2.2. Related Degree Programs.................................................................................................................................... 5
2.3. Program Description, Purposes and Planning ..................................................................................................... 5
2.4. Admissions .......................................................................................................................................................... 8
2.5. Academic and Other Support Services ............................................................................................................... 8
2.6. Prior Learning Assessment ................................................................................................................................. 8
2.7. Program Assessment and Improvement .............................................................................................................. 8
Section 3. Program Schedule and Curriculum ....................................................................................................................... 9
Section 4. Faculty ................................................................................................................................................................. 12
Section 5. Financial Resources and Instructional Facilities ................................................................................................... 1
Section 6. Library Resources ................................................................................................................................................. 1
1Use a different form if the proposed new program will lead to a graduate degree or any credit-bearing certificate; be a combination of
existing registered programs (i.e. for a multi-award or multi-institution program); be a breakout of a registered track or option in an
existing registered program; or lead to certification as a classroom teacher, school or district leader, or pupil personnel services
professional (e.g., school counselor).
2This email address limits attachments to 25 MB. If a file with the proposal and appended materials exceeds that limit, it should be
emailed in parts.
Section 7. External Evaluation ............................................................................................................................................... 1
Section 8. Institutional Response to External Evaluator Reports........................................................................................... 1
Section 9. SUNY Undergraduate Transfer............................................................................................................................. 1
Section 10. Application for Distance Education .................................................................................................................... 2
Section MPA-1. Need for Master Plan Amendment and/or Degree Authorization ............................................................... 2
List of Appended Items ........................................................................................................................................................... 2
Section 1. General Information
a)
Institutional
Information
Date of Proposal: 9/27/19
Institution’s 6-digit SED Code: 210500
Institution’s Name: University at Albany
Address: 1400 Washington Avenue, Albany, NY 12222
Dept of Labor/Regent’s Region: Capital District
b)
Program
Locations
List each campus where the entire program will be offered (with each institutional or branch campus
6-digit SED Code): 210500
List the name and address of off-campus locations (i.e., extension sites or extension centers) where
courses will offered, or check here [ X ] if not applicable:
c)
Proposed
Program
Information
Program Title: Machine Learning
Award(s) (e.g., Certificate.): Advanced Certificate
Number of Required Credits: Minimum [ 9 ] If tracks or options, largest minimum [ ]
Proposed HEGIS Code: 1703
Proposed 6-digit CIP 2010 Code: 27.0301
If the program will be accredited, list the accrediting agency and expected date of accreditation:
If applicable, list the SED professional licensure title(s)3 to which the program leads:
d)
Campus
Contact
Name and title: Jonathan Bartow, Vice Dean for Graduate Education
Telephone: 518-437-5062
E-mail: jbartow@albany.edu
e)
Chief Executive
or Chief
Academic
Officer
Approval
Signature affirms that the proposal has met all applicable campus administrative and shared governance
procedures for consultation, and the institution’s commitment to support the proposed program.
E-signatures are acceptable.
Name and title: Carol Kim, Ph.D. Provost and Senior Vice President for Academic Affairs
Signature and date:
If the program will be registered jointly4 with one or more other institutions, provide the following
information for each institution:
Partner institution’s name and 6-digit SED Code:
Name, title, and signature of partner institution’s CEO (or append a signed letter indicating approval of
this proposal):
3 If the proposed program leads to a professional license, a specialized form for the specific profession may need to accompany this proposal.
4 If the partner institution is non-degree-granting, see SED’s CEO Memo 94-04.
Section 2. Program Information
2.1. Program Format
Check all SED-defined formats, mode and other program features that apply to the entire program.
a) Format(s): [ ]Day [ ]Evening
[ ]Weekend
[ ]Evening/Weekend
[ ]Not Full-Time
b) Modes: [ ]Standard [ ]Independent Study [ ]External [ ]Accelerated [X]Distance Education
NOTE: If the program is designed to enable students to complete 50% or more of the course requirements through
distance education, check Distance Education, see Section 10, and append a Distance Education Format Proposal
c) Other: [ ] Bilingual [ ] Language Other Than English [ ] Upper Division [ ] Cooperative [ ] 4.5 year [ ] 5 year
2.2. Related Degree Programs
All coursework required for completion of the certificate or advanced certificate program must be applicable to
a currently registered degree program at the institution (with the possible exception of post-doctoral certificates
in health-related fields). Indicate the registered degree program(s) by title, award and five-digit SED Inventory
of Registered Programs (IRP) code to which the credits will apply:
MS in Data Science, program code 39238.
2.3. Program Description, Purposes and Planning
a) What is the description of the program as it will appear in the institution’s catalog?
Graduate Certificate in Machine Learning:
This program is designed to provide students with the foundation of Machine Learning. Students will also
develop practical working skills in this area.
The program requires 9 credits with an average grade of B or higher.
Requirements for admission: undergraduate degree (which is not necessarily in mathematics) and knowledge
of calculus through a semester of multivariable calculus.
Program Description:
Required courses consist of AMAT 590 – Function Theory and Functional Analysis for Applications; AMAT
591 – Optimization Methods and Non-linear Programming; AMAT 592 – Methods of Machine Learning. This
material builds a solid foundation for application of Machine Learning methods in practice and research.
b) What are the program’s educational and, if appropriate, career objectives, and the program’s primary student learning
outcomes (SLOs)? NOTE: SLOs are defined by the Middle States Commission on Higher Education in the
Characteristics of Excellence in Higher Education (2006) as “clearly articulated written statements, expressed in
observable terms, of key learning outcomes: the knowledge, skills and competencies that students are expected to exhibit
upon completion of the program.”
Graduates of this program will attain the following educational and career objectives:
1) They will understand the fundamental principles and theories of Machine Learning
2) They will have the ability to critically analyze data in practice using major Machine Learning concepts and
contemporary software packages.
3) They will display the knowledge and skills sufficient to establish a career in Machine Learning as a
mathematical tool for Artificial Intelligence.
Student Learning Outcomes:
Knowledge: through coursework, students will acquire foundational knowledge of the discipline of Machine
Learning.
Skills: through coursework, students will learn to manipulate data sets with appropriate software, to ask
appropriate questions about the data, and to follow up their initial analyses with further questions and
investigations.
c) How does the program relate to the institution’s and SUNY’s mission and strategic goals and priorities? What is the
program’s importance to the institution, and its relationship to existing and/or projected programs and its expected
impact on them? As applicable, how does the program reflect diversity and/or international perspectives?
The program addresses Priority #1 of the University Strategic plan: Invest in academic programs—both in-person
and online—that balance emerging demands of students, employers, and society while cultivating intellectual
development, ethical reasoning, and practical skills.
The proposed program fills the current gap in training work force proficient in contemporary methods of both theoretical
and applied machine learning when working with big data sets. A number of applications to Artificial Intelligence
problems makes the program a valuable source for those interested in mathematical methods of AI. The previous
experience of offering individual courses of this program makes us expect that it will be very attractive for international
students.
d) How were faculty involved in the program’s design?
The program was designed by the faculty who will be teaching the courses. They worked together to choose the material
included in the program.
How did input, if any, from external partners (e.g., educational institutions and employers) or standards influence the
program’s design? If the program is designed to meet specialized accreditation or other external standards, such as the
educational requirements in Commissioner’s Regulations for the Profession, append a side-by-side chart to show how
the program’s components meet those external standards. If SED’s Office of the Professions requires a specialized form
for the profession to which the proposed program leads, append a completed form at the end of this document.
The program is fully supported by the University at Albany, there is no input from external partners. No specialized
forms required.
e) Enter anticipated enrollments for Years 1 through 5 in the table below. How were they determined, and what
assumptions were used? What contingencies exist if anticipated enrollments are not achieved?
The numbers in the table below are based on previous enrollments in the courses included in the program when taught in
both face-to-face and online formats.
Year
Anticipated Headcount Enrollment
Estimated
FTE
Full-time
Part-time
Total
1
0
15
15
2
0
25
25
3
0
30
30
4
0
35
35
5
0
40
40
f) Outline all curricular requirements for the proposed program, including prerequisite, core, specialization (track,
concentration), capstone, and any other relevant component requirements, but do not list each General Education course.
Course Title
Credits
AMAT 590 Function Theory and Functional Analysis for Applications
(prerequisites: Basic Linear Algebra AMAT 220 or equivalent, Calculus of
Several Variables , AMAT 214 or equivalent)
3
AMAT 591 Optimization Methods and Nonlinear Programming
(prerequisite: AMAT 590)
3
AMAT 592 Machine Learning (prerequisites: AMAT 591 and AMAT 554
or instructor’s permission)
3
Total required credits: 9
h) Program Impact on SUNY and New York State
h)(1) Need: What is the need for the proposed program in terms of the clientele it will serve and the educational and/or
economic needs of the area and New York State? How was need determined? Why are similar programs, if any,
not meeting the need?
There are no similar programs either in New York State or nationally. We are providing a unique training
program here. It gives training in new Machine Learning techniques in data science with deep coverage of the
underlying mathematical concepts and methods. Managers of local companies expressed the desire to retrain their
workforce in these powerful methods of data analysis. It will allow certificate holders to find positions in the
actuarial field, the insurance industry, financial institutions, state government, biomedical research, etc.
h)(2) Employment: For programs designed to prepare graduates for immediate employment, use the table below to list
potential employers of graduates that have requested establishment of the program and describe their specific
employment needs. If letters from employers support the program, they may be appended at the end of this form.
As appropriate, address how the program will respond to evolving federal policy on the “gainful employment” of
graduates of certificate programs whose students are eligible for federal student assistance.
Employer
Need: Projected positions
In initial year
In fifth year
The program is not designed with specific employers in mind. It is rather directed to the growing needs of academia
and industry in work force trained to deal with big data sets using the most contemporary mathematical tools.
h)(3) Similar Programs: Use the table below to list similar programs at other institutions, public and independent, in
the service area, region and state, as appropriate. Expand the table as needed. NOTE: Detailed program-level
information for SUNY institutions is available in the Academic Program Enterprise System (APES) or Academic
Program Dashboards. Institutional research and information security officers at your campus should be able to
help provide access to these password-protected sites. For non-SUNY programs, program titles and degree
information – but no enrollment data – is available from SED’s Inventory of Registered Programs.
Institution
Program Title
Degree
Enrollment
None
h)(4)
Collaboration: Did this program’s design benefit from consultation with other SUNY campuses? If so, what
was that consultation and its result?
No.
h)(5)
Concerns or Objections: If concerns and/or objections were raised by other SUNY campuses, how were they
resolved? Not applicable.
2.4. Admissions
a) What are all admission requirements for students in this program? Please note those that differ from the institution’s
minimum admissions requirements and explain why they differ.
Students are admitted with various undergraduate majors in mathematics, science and social sciences. In
addition to the general University requirements for admission to graduate studies, students who are deficient in
their mathematical preparation must make up such deficiencies before being formally admitted into the
program.
b) What is the process for evaluating exceptions to those requirements?
Review by the Graduate Committee of our department, which is composed of full-time faculty.
c) How will the institution encourage enrollment in this program by persons from groups historically underrepresented in the
institution, discipline or occupation?
We will advertise in multiple ways, including posters, advertisements and recruiting talks. We also use the same
advertisement tools we currently use for our Master’s program in Data Science to attract students from mathematics
science departments at Historically Black Colleges and Universities.
2.5. Academic and Other Support Services
Summarize the academic advising and support services available to help students succeed in the program.
Two faculty members who will teach online courses for this program will provide advisement to the students enrolled in the
program.
2.6. Prior Learning Assessment
If this program will grant credit based on Prior Learning Assessment, describe the methods of evaluating the learning and the
maximum number of credits allowed, or check here [ x ] if not applicable.
2.7. Program Assessment and Improvement
Describe how this program’s achievement of its objectives will be assessed, in accordance with SUNY policy,
including the date of the program’s initial assessment and the length (in years) of the assessment cycle. Explain
plans for assessing achievement of students’ learning outcomes during the program and success after
completion of the program. Append at the end of this form, a plan or curriculum map showing the courses
in which the program’s educational and, if appropriate, career objectives – from Item 2.3(b) of this form – will
be taught and assessed. NOTE: The University Faculty Senate’s Guide for the Evaluation of Undergraduate
Programs is a helpful reference.
The assessment will be performed along with the assessments of the other graduate programs in the Department
of Mathematics and Statistics. We will also track the employment records of those students graduated from the
program who will be seeking an employment, and records of promotion of those currently employed and
working on the obtaining of his certificate to enhance their skills. Consistent with University policy, our
Department maintains a 7-year assessment cycle for its programs. The Department will apply the same
methodology to the assessment of the Graduate Certificate Program in Machine Learning that it performs in the
assessment of all its programs. This will include direct assessment through student work in the courses, indirect
assessment through student surveys, and indirect assessment through student focus groups. The assessment
methods will identify successes and deficiencies in the program, and we will use assessment results to address
deficiencies and build and maintain program strength and make sure that learning objectives are met.
Section 3. Program Schedule and Curriculum
Complete the SUNY Program Schedule for Certificate and Advanced Certificate Programs to show how a typical
student may progress through the program.
NOTE: For an undergraduate certificate program, the SUNY Program Schedule for Certificate and Advanced
Certificate Programs must show all curricular requirements and the number of terms required to complete them.
Certificate programs are not required to conform to SUNY’s and SED’s policies on credit limits, general education,
transfer and liberal arts and sciences.
EXAMPLE FOR ONE TERM: Program Schedule for Certificate Program
Term 2: Fall 20xx
Course Number & Title
Cr
New
Prerequisite(s)
ACC 101 Principles of Accounting
4
MAT 111 College Mathematics
3
MAT 110
CMP 101 Introduction to Computers
3
HUM 110 Speech
3
X
ENG 113 English 102
3
Term credit total:
16
NOTE: For a graduate advanced certificate program, the SUNY Sample Program Schedule for Certificate and
Advanced Certificate Programs must include all curriculum requirements. The program is not required to conform with
the graduate program expectations from in Regulation 52.2 http://www.highered.nysed.gov/ocue/lrp/rules.htm.
a) If the program has fewer than 24 credit hours, or if the program will be offered through a nontraditional
schedule (i.e., not on a semester calendar), what is the schedule and how does it impact financial aid
eligibility? NOTE: Consult with your campus financial aid administrator for information about
nontraditional schedules and financial aid eligibility.
The program will be offered during summer and winter sessions. There will be no impact on financial
aid eligibility.
b) For each existing course that is part of the proposed undergraduate certificate or the graduate advanced certificate,
append, at the end of this form, a catalog description.
See Appendix 1.
c)For each new course in the certificate or advanced certificate program, append a syllabus at the end of this document.
No new courses.
d)If the program requires external instruction, such as clinical or field experience, agency placement, an internship,
fieldwork, or cooperative education, append a completed External Instruction form at the end of this document.
N/A
SUNY Program Schedule for Certificate and Advanced Certificate Programs
Program/Track Title and Award:___Graduate Certificate Program in Machine Learning___________________________________________________
−
Indicate academic calendar type: [ ] Semester [ ] Quarter [ ] Trimester [ X ] Other (describe): online during Summer and Winter sessions
−
Label each term in sequence, consistent with the institution’s academic calendar (e.g., Fall 1, Spring 1, Fall 2)
−
Use the table to show how a typical student may progress through the program; copy/expand the table as needed. Complete all columns that apply to a course.
Summer 1:
Winter 1:
Course Number & Title
Credits
New (X)
Co/Prerequisites
Course Number & Title
Credits
New (x)
Co/Prerequisites
AMAT 590 Function Theory and Functional
Analysis for Applications
3
Basic linear algebra, calculus
AMAT 592 Methods of Machine Learning
3
AMAT 591
AMAT 591 Optimization methods and
Nonlinear Programming
3
AMAT 590
Term credit totals:
Term credit totals:
Term 3:
Term 4:
Course Number & Title
Credits
New (X)
Co/Prerequisites
Course Number & Title
Credits
New (X) Co/Prerequisites
Term credit totals:
Term credit totals:
Program Totals (in credits):
Total
Credits: 9
Section 4. Faculty
a) Complete the SUNY Faculty Table on the next page to describe current faculty and to-be-hired (TBH) faculty.
b) Append at the end of this document position descriptions or announcements for each to-be-hired faculty member.
NOTE: CVs for all faculty should be available upon request. Faculty CVs should include rank and employment status,
educational and employment background, professional affiliations and activities, important awards and recognition,
publications (noting refereed journal articles), and brief descriptions of research and other externally funded projects.
New York State’s requirements for faculty qualifications are in http://www.highered.nysed.gov/ocue/lrp/rules.htm.
c) What is the institution’s definition of “full-time” faculty?
All faculty in this program are full-time tenured faculty who, in addition to maintaining an active research
program and advising doctoral students, teach 2 courses each semester.
SUNY Faculty Table
Provide information on current and prospective faculty members (identifying those at off-campus locations) who will be expected to teach any course in the
graduate program. Expand the table as needed. Use a separate Faculty Table for each institution if the program is a multi-institution program.
(a)
(b)
(c)
(d)
(e)
(f)
Faculty Member Name and
Title/Rank
(Include and identify Program
Director with an asterisk)
% of Time
Dedicated
to This
Program
Program
Courses
Which May Be
Taught
(Number and
Title)
Highest and Other
Applicable Earned
Degrees (include
College or
University)
Discipline(s) of
Highest and Other
Applicable Earned
Degrees
Additional Qualifications: List
related certifications, licenses and
professional experience in field
PART 1. Full-Time Faculty
*Stessin, Prof.
10%
AMAT 590,
Function theory
and Functional
analysis for
applications
PH.D. Moscow State
University
Mathematics
Ying, Assoc. Prof.
20%
AMAT 591,
Optimization
methods and
nonlinear
programming,
AMAT 592,
Machine
Learning
Ph.D. Zhejiang
University
Mathematics
Part 2. Part-Time Faculty
(a)
(b)
(c)
(d)
(e)
(f)
Faculty Member Name and
Title/Rank
(Include and identify Program
Director with an asterisk)
% of Time
Dedicated
to This
Program
Program
Courses
Which May Be
Taught
(Number and
Title)
Highest and Other
Applicable Earned
Degrees (include
College or
University)
Discipline(s) of
Highest and Other
Applicable Earned
Degrees
Additional Qualifications: List
related certifications, licenses and
professional experience in field
Part 3. Faculty To-Be-Hired (List as
TBH1, TBH2, etc., and provide
title/rank and expected hiring date)
Section 5. Financial Resources and Instructional Facilities
a) What is the resource plan for ensuring the success of the proposed program over time? Summarize the instructional
facilities and equipment committed to ensure the success of the program. Please explain new and/or reallocated
resources over the first five years for operations, including faculty and other personnel, the library, equipment,
laboratories, and supplies. Also include resources for capital projects and other expenses.
Presently we have enough faculty to teach these classes. Additional faculty will require when the enrollment exceed the
size of two sections. We anticipate that these additional faculty will be required the third year and on since the
initialization of the program.
b) Complete the five-year SUNY Program Expenses Table, below, consistent with the resource plan summary. Enter the
anticipated academic years in the top row of this table. List all resources that will be engaged specifically as a result of
the proposed program (e.g., a new faculty position or additional library resources). If they represent a continuing cost,
new resources for a given year should be included in the subsequent year(s), with adjustments for inflation or negotiated
compensation. Include explanatory notes as needed.
SUNY Program Expenses Table
(OPTION: You can paste an Excel version of this schedule AFTER this sentence, and delete the table below.)
Program Expense Categories
Expenses (in dollars)
Before
Start
Academic
Year 1:
Academic
Year 2:
Academic
Year 3:
Academic
Year 4:
Academic
Year 5:
(a) Personnel (including
faculty and all others)*
9,500
14,000
18,500
18,500
18,500
(b) Library
(c) Equipment
(d) Laboratories
(e) Supplies
(f) Capital Expenses
(g) Other (Specify):
(h) Sum of Rows Above
9500
14000
18500
18500
18500
• Adjunct faculty will not be involved with this program.
Section 6. Library Resources
NOTE: This section does not apply to certificate or advanced certificate programs.
Section 7. External Evaluation
NOTE: This section does not apply to certificate or advanced certificate programs.
Section 8. Institutional Response to External Evaluator Reports
NOTE: This section does not apply to certificate or advanced certificate programs.
Section 9. SUNY Undergraduate Transfer
NOTE: This section does not apply to certificate or advanced certificate programs.
Section 10. Application for Distance Education
a) Does the program’s design enable students to complete 50% or more of the course requirements through distance
education? [ ] No [ X ] Yes. If yes, append a completed SUNY Distance Education Format Proposal at the end of
this proposal to apply for the program to be registered for the distance education format.
b) Does the program’s design enable students to complete 100% of the course requirements through distance education? [ ]
No [ X ] Yes
Section MPA-1. Need for Master Plan Amendment and/or Degree Authorization
NOTE: This section does not apply to certificate or advanced certificate programs.
List of Appended Items
Appended Items: Materials required in selected items in Sections 1 through 5 and Section 10 of this form should be
appended after this page, with continued pagination. In the first column of the chart below, please number the appended
items, and append them in number order.
Number
Appended Items
Reference Items
For multi-institution programs, a letter of approval from partner
institution(s)
Section 1, Item (e)
For programs leading to professional licensure, a side-by-side chart showing
how the program’s components meet the requirements of specialized
accreditation, Commissioner’s Regulations for the Profession, or other
external standards
Section 2.3, Item (e)
For programs leading to licensure in selected professions for which the SED
Office of the Professions (OP) requires a specialized form, if required by OP
Section 2.3, Item (e)
OPTIONAL: For programs leading directly to employment, letters of
support from employers, if available
Section 2, Item 2.3 (h)(2)
1
For all programs, a plan or curriculum map showing the courses in which the
program’s educational and (if appropriate) career objectives will be taught and
assessed
Section 2, Item 7
2
For all programs, a catalog description for each existing course that is part of
the proposed program
Section 3, Item (b)
For all programs, syllabi for all new courses in the proposed program
Section 3, Item (c)
For programs requiring external instruction, External Instruction Form and
documentation required on that form
Section 3, Item (d)
For programs that will depend on new faculty, position descriptions or
announcements for faculty to-be-hired
Section 4, Item (b)
3
For programs designed to enable students to complete at least 50% of the
course requirements at a distance, a Distance Education Format Proposal
Section 10
Appendix 1. Chart linking learning objectives to the specific courses
Objective number
Course
1) Graduates will understand the
fundamental principles and
theories of Machine Learning
AMAT 590, AMAT 591, AMAT 592
2) Graduates will have the ability to
critically analyze data in practice
using major Machine Learning
AMAT 592
concepts and contemporary
software packages
3) Graduates will display the
knowledge and skills sufficient to
establish a career in Machine
Learning as a mathematical tool
for Artificial Intelligence
AMAT 592
Appendix 2:
Catalog description for each existing course that is part of the proposed program.
Mat 590 Function Theory and Functional Analysis for Applications (3)
This course covers function analytic aspects necessary for applications in various areas of science and
engineering, notably in Data Science. Among main topics of the course are: elementary theory of Lebesgue
measure and integration, spaces of Lebesgue integrable functions, Banach spaces and Hanh-Banach theorem,
duality in Banach spaces, Hilbert spaces, reproducing kernel Hilbert spaces, non-linear analysis in Banach
spaces. Prerequisites: Basic linear algebra, e.g., AMAT 220; calculus of several variables, e.g., AMAT 214
Mat 591 Optimization Methods and Nonlinear Programming (3)
Modern methods in convex optimization and nonlinear programming. Newton's method, gradient descent, linear
programming, quadratic optimization, semidefinite programming and related topics. Prerequisites: AMAT590.
Mat 592 Machine Learning (3)
The primary goal of this course is to provide students with statistical tools and mathematical principles needed to
solve both the traditional and modern data science problems encountered in practice. In particular, the course
covers a wide variety of topics in machine learning. It introduces the key terms, concepts and methods in
machine learning, with an emphasis on developing critical analytical skills through hands-on exercises of actual
data analysis tasks. At the same time, it will cover modern machine learning topics such as boosting and online
learning for large-scale data analysis. In addition, the students will practice basic programming skills to use
software tools in machine learning. Prerequisites: AMAT591 and AMAT 554.
Distance Education Format Proposal
For A Proposed or Registered Program
Form 4
Version 2014-11-17
When a new or existing program is designed for a distance education format, a campus Chief Executive Officer or Chief
Academic Officer should submit a signed cover letter and this completed form to the SUNY Provost at
program.review@suny.edu. According to MSCHE, the 50% standard includes only courses offered in their entirety via
distance education, not courses utilizing mixed delivery methods. Also, MSCHE requires that the first two programs for
which 50% or more is offered through distance education be submitted for Commission review and prior approval of a
substantive change.
•
All campuses must complete the following sections: Sections 1 - 3, and Part B: Program Specific Issues.
•
Part A must be completed if the proposing campus has not previously submitted this form with a completed Part A:
Institution-wide Issues, or has made significant changes to its institution-wide distance education operations since last
completing Part A. This applies even if the institution has programs registered to be delivered at a distance.
Section 1. General Information
a)
Institutional
Information
Institution’s 6-digit SED Code: 210500
Institution’s Name: University at Albany
Address: 1400 Washington Ave, Albany NY 12222
b)
Registered or
Proposed Program
Program Title: Machine Learning
SED Program Code
Award(s) (e.g., A.A., B.S.): Advanced Certificate
Number of Required Credits: Minimum [ 9 ] If tracks or options, largest minimum [ ]
HEGIS Code: 1703
CIP 2010 Code: 27.0301
c)
Distance
Education Contact
Name and title: Jonathan Bartow, Vice Dean for Graduate Education
Telephone: 518-437-5062
E-mail: jbartow@albany.edu
d)
Chief Executive or
Chief Academic
Officer Approval
Signature affirms that the proposal has met all applicable campus administrative and shared
governance procedures for consultation, and the institution’s commitment to support the proposed
program. E-signatures are acceptable.
Name and title: Carol Kim, Ph.D. Provost and Senior Vice President for Academic Affairs
Signature and date:
If the program will be registered jointly5 with one or more other institutions, provide the
following information for each institution:
Partner institution’s name and 6-digit SED Code:
Name, title, and signature of partner institution’s CEO (or append a signed letter indicating
approval of this proposal):
5 If the partner institution is non-degree-granting, see SED’s CEO Memo 94-04.
Section 2: Enrollment
Year
Anticipated Headcount Enrollment
Estimated
FTE
Full-time
Part-time
Total
1
10
5
15
2
15
10
25
3
20
10
30
4
25
10
35
5
30
10
40
Section 3: Program Information
a) Term length (in weeks) for the distance program: 4 weeks
b) Is this the same as term length for classroom program? [ X ] No [ ] Yes
c) How much "instructional time" is required per week per credit for a distance course in this program? (Do not
include time spent on activities that would be done outside "class time," such as research, writing assignments, or
chat rooms.) NOTE: See SUNY policy on credit/contact hours and SED guidance.
15 hours per week
d) What proportion or percentage of the program will be offered in Distance Education format? Will students be able
to complete 100 percent of the program online? If not, what proportion will be able to be completed online?
100%
e) What is the maximum number of students who would be enrolled in an online course section?
15 students per section. Multiple sections are possible.
Part A: Institution-wide Issues: Submit Part A only for the first Distance Education program proposed by your
institution using this form. SUNY and the State Education Department will keep this in a master file so that your
institution will not need to resubmit it for each new proposed online program, unless there are significant changes, such
as a new platform.
Part A.1. Organizational Commitment
a) Describe your institution’s planning process for Distance Education, including how the need for distance access was
identified, the nature and size of the intended audiences, and the provisions for serving those audiences, including
how each student’s identity will be verified.
b) Describe your institution’s resources for distance learning programs and its student and technical support services to
ensure their effectiveness. What course management system does your institution use?
c) Describe how the institution trains faculty and supports them in developing and teaching online courses, including
the pedagogical and communication strategies to function effectively. Describe the qualifications of those who train
and/or assist faculty, or are otherwise responsible for online education.
d) If your institution uses courses or academic support services from another provider, describe the process used (with
faculty participation) to evaluate their quality, academic rigor, and suitability for the award of college credit and a
degree or certificate.
e) Does your institution have a clear policy on ownership of course materials developed for its distance education
courses? How is this policy shared with faculty and staff? NOTE: You may refer to SUNY’s statement on copyright
and faculty ownership of instructional content, and/or faculty contract provisions.
Part A.2. Learner Support
a)
Describe how your institution provides distance students with clear information on:
Program completion requirements
To successfully complete the program a student must pass all three courses with grades not below than B-
and have total GPA 3.0 or above.
The nature of the learning experience
Students will take online lectures, do homework assignments, and pass exams.
Any specific student background, knowledge, or technical skills needed
Calculus courses AMAT 112, AMAT 214 and an undergraduate Linear Algebra (AMA 220).
Expectations of student participation and learning
Students are supposed to study the Lectures material provided for them, participate in bulletin board
discussions, and perform in time the required classwork.
The nature of interactions among faculty and students in the courses.
Students are supposed to communicate with the instructor via bulletin board discussions and via e-mail
messages.
Any technical equipment or software required or recommended.
No special equipment is required.
b)
Describe how your institution provides distance learners with adequate academic and administrative support,
including academic advisement, technical support, library and information services, and other student support
services normally available on campus. Do program materials clearly define how students can access these support
services?
SUNY Albany supports Blackboard platform for the program and provides technical consultations for courses
development.
c)
Describe how administrative processes such as admissions and registration are made available to distance students,
and how program materials inform students how to access these services.
The admission and registration for the program courses is maintained by the Office of Summer Sessions. All
appropriate material is available through the University web page.
d)
What orientation opportunities and resources are available for students of distance learning?
For each course of the program the instructor prepares and posts on board course description and detailed
information on course requirements, procedures, and schedules.
Part B: Program-Specific Issues: Submit Part B for each new request to add Distance Education Format to a proposed
or registered program.
Part B.1. Learning Design
a)
How does your institution ensure that the same academic standards and requirements are applied to the program on
campus and through distance learning? If the curriculum in the Distance Education program differs from that of the
on-ground program, please identify the differences.
The curriculum of each course of this program is identical to the one offered in the face-to-face format and typically
is taught by the same faculty. The same academic standards are assumed for the online program.
b)
Are the courses that make up the distance learning program offered in a sequence or configuration that allows timely
completion of requirements?
Each course of the program is planned to be offered every Summer and Winter session which allows student
necessary flexibility to complete the program in a timely manner.
c)
How do faculty and others ensure that the technological tools used in the program are appropriate for the content
and intended learning outcomes?
The courses of the program are supported by the Blackboard platform, which proved to be an effective and reliable
venue for this kind of classes.
d)
How does the program provide for appropriate and flexible interaction between faculty and students, and among
students?
Bulletin board is proven to be a successful way of communication in addition to regular e-mail correspondence.
e)
How do faculty teaching online courses verify that the student who registers in a distance education course or
program is the same student who participates in and completes the course or program and receives the academic
credit?
The University at Albany utilizes two layers of authorization and authentication for students who participate in
online learning. Students are required to establish an account and to log in to the University password protected domain
using the NETID protocol and must also log into the Blackboard Learning Management System using their university
credentials. Blackboard also uses Safe Assign as a tool to monitor the completion of certain tasks within the LMS
environment.
.
Part B.2. Outcomes and Assessment
a) Distance learning programs are expected to produce the same learning outcomes as comparable classroom-based
programs. How are these learning outcomes identified – in terms of knowledge, skills, or credentials – in course and
program materials?
The online courses are routinely offered on campus. They follow the same syllabi outlining necessary of knowledge,
skills, or credentials in course and program materials
b) Describe how the means chosen for assessing student learning in this program are appropriate to the content,
learning design, technologies, and characteristics of the learners.
Assessing student learning in the program will be equivalent to assessing the student in a face-to- face environment.
Neither our classroom versions nor our online courses rely on high-stakes exams that are incompatible with the
asynchronous online pedagogy employed in the program. All course activities can be successfully completed and
assessed online. In general learners are required to demonstrate developing understanding through a variety of
assessments that include written work in various forms with formative and summative feedback provided by the
instructor.
Part B.3. Program Evaluation
a) What process is in place to monitor and evaluate the effectiveness of this particular distance education
program on a regular basis?
The Department of Mathematics and Statistics has a yearly evaluation process, including course surveys, graduation
surveys and monitoring of course grades. This program will use the same evaluation system as the other accredited
degrees.
b) How will the evaluation results will be used for continuous program improvement?
The Graduate Director routinely reviews evaluation results and arranges for mentoring and other supports where
needed to improve instruction. The department faculty members routinely discuss the courses and programs drawing on
evaluation results, to discuss any needed improvements.
c) How will the evaluation process assure that the program results in learning outcomes appropriate to the
rigor and breadth of the college degree or certificate awarded?
The program evaluation is the same for students taking online or campus based courses. Most of these courses are
already part of programs with national accreditation. Therefore they meet university requirements for rigor and breadth
required of graduate coursework, including credits, format, and assignments needed for a graduate degree.
Part B.4. Students Residing Outside New York State
SUNY programs must comply with all “authorization to operate" regulations that are in place in other U.S. states where the
institution has enrolled students or is otherwise active, based on each state’s definitions.
a) What processes are in place to monitor the U.S. state of residency of students enrolled in any distance education
course in this program while residing in their home state?
Distance learning students will be flagged in our integrated administrative system. This will allow regular querying
so that we can identify any our of state students who participate from their home state. We can then seek approval from their
home state if necessary.
b) Federal regulations require institutions delivering courses by distance education to provide students or prospective
students with contact information for filing complaints with the state approval or licensing entity in the student’s
state of residency and any other relevant state official or agency that would appropriately handle a student's
complaint. What is the URL on your institution’s website where contact information for filing complaints for
students in this program is posted? NOTE: Links to information for other states can be found at here.
www.albany.edu/ir/rtk/
NOTE: Links to information for other states can be found at http://system.suny.edu/academic- affairs/distance-
learning/
