University of Toronto Major Modification Proposal Type A:Significant modifications to existing undergraduate programs

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Major Modification Proposal
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Type A: Significant Modification to Existing Program

University of Toronto

Major Modification Proposal


Type A:


Significant modifications to existing
undergraduate
programs



Section 1

Program being modified:

Specialist in
Quantitative Analysis

and

Specialist (Co
-
operative) in Quantitative
Analysis

Department / (graduate) Unit (if applicable) where
the program is housed:

Department of Computer and
Mathematical Sciences

Faculty / Academic Division:

University of Toronto
Scarborough

Faculty / Academic Division contact:

Annette Knott
, Academic Programs Officer

aknott@utsc.utoronto.ca

Department / Unit contact:

Michael Evans

mevans@utstat.utoronto.ca

Anticipated Effective date:

July 1, 2013

Version Date:

August 16, 2012











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Type A: Significant Modification to
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Section 2



1.

Overview

of the Proposed Major Modification(s)


The Department of Computer and Mathematical Sciences
currently offer
s

a Specialist Program
in Quantitative Analysis
, which has

four
streams
: Biological and Life Sciences; Physical
Sciences; Mathematical Finance, Management and Economics; and Social and Health Sciences.
Of these streams,
Mathematical Finance, Management and Economics

has been the most
successful at attracting
many
excellent st
udents. This proposal brings fo
rward a number of
changes to this

existing program to refine its focus
,

and strengthen its appeal.


Specifically, we propose to:


1.

R
ename the program

as

Specialist in Statistics
*
.

2.

Restructure the program:



Eliminate t
he Biol
ogical and Life Sciences;
Physical Sciences
;

and Social and Health
Sciences streams because they have not attracted sufficient numbers of students
.



Introduce significant enhancements to the existing Mathematical Finance, Management
and Economics stream
;

rename this stream as Quantitative Finance
.



Introduce a new stream to the program:

Machine Learning and Data Mining.



Structure the program such

that
both streams of the program (
Quantitative Finance and
Machine Learning and Data Mining
)

share a common cor
e of courses
.

Students will
continue to choose a stream in their second year of study.



CSCA67H and MATB44H will move from the common core to the Quantitative Finance
stream.




CSCB07H, CSCB63H

and CSCB63H will move from the common core to the Machine
Learni
ng and Data Mining stream.



Add the following courses (as either requirements or options) to the common core:
MATA30H, MATA36H, MATB61H, CSCC37H, STAC62H, STAC67H and STAD37H.



Apply these changes to the Co
-
op analog of this program
.


*Note:
The existing Maj
or and Minor programs in Statistics will offer a subset of the common
core of the Specialist program.


These changes are line with recommendations from the

Fall 2011 E
xternal
Review
of the
Department
, which

highlighted a

need to address
concerns
associated with the current
Quantitative Analysis
Specialist
program
,

and

which also

suggested we

introduce a program in
Machine Learning

and Data Mining
. Both streams of the
revised
program are targeted at fields
that are in high
-
demand areas by industry
,

and also lead to further study at the graduate level.


The learning outcomes of the
revised
program are described below
:


1.

In addition to providing a sound understanding of modern statistical theory and methodology,
the Quantitative Finance stream will giv
e students a sound theoretical understanding of the
core concepts of quantitative finance. In ACTB40H3

[Fundamentals of Investment and
Credit]

students will study the concept of the time value of money and its various
applications. In
the new course
STAB41
H3

[Financial Derivatives]

the no arbitrage principle


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Major Modification Proposal
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Type A: Significant Modification to
Existing Program


and its consequences for risk
-
neutral pricing in the binomial pricing model will be studied.
STAC70H3

[Statistics and Finance]

will extend this to a rigorous development of stochastic
calculus and its
application to risk
-
neutral pricing to obtain, for example, the Black
-
Scholes
result. In STAD70H3

[Statistics and Finance II]

portfolio theory, the capital asset pricing
model and value
-
at
-
risk measures will be developed. In all these courses practical and

computational aspects of the concepts will be emphasized so that students graduate ready to
apply their skills.

2.

In addition to providing a sound understanding of modern statistical theory and methodology,
the Statistical Machine Learning and Data Mining s
tream will train students in the core
concepts of machine learning through the courses CSCC11H3
[Introduction to
Machine
Learning and Data Mining
]

and

the new course

STAD68H3
[
Advanced Machine Learning
and Data Mining
]
. Through these courses, together with

the additional Statistics and some
Computer Science courses, students will develop a deep understanding of the latest concepts
in machine learning and the ability to implement these in contexts of practical importance.


There will be no essential differen
ces to the physical resources being used to deliver the
revised
program. The faculty complement of
Statistics
at UTSC was recently increased, and the
revised
program
has been
designed to be delivered with the existing faculty resources.


2.

Academic
Rationale

.

The Department of Computer and Mathematical Sciences at the University of Toronto
Scarborough
currently offer
s

a
Specialist
program in Quantitative Analysis
, which has

four
streams
:
Biological and Life Sciences; Physical Sciences; Mathematical
Finance, Management
and Economics; and Social and Health Sciences
.
O
f these streams, that in
Mathematical Finance,
Management and Economics
has been

the

most successful

at

attracting m
any excellent

students
.

This proposal brings forward a number of changes

to the existing program to refine its focus and
strengthen its appeal
to
students
.



The proposed changes effectively address recommendations made in the 2011 Ex
ternal Review
of the Department, which highlighted a need to address concerns associated with

the current
Quantitative Analysis Specialist program, and which also suggested we introduce a program in
Machine Learning and Data Mining. Both streams of the revised program are targeted at fields
that are in high
-
demand areas by industry, and also lead
to further study at the graduate level.


First, this proposal seeks approval to change the name of the program from

Specialist in
Quantitative Analysis to Specialist in Statistics.

The term “Quantitative Analysis” is not
commonly used, so by
renaming the
program

we will
better identif
y its

content to students,
and
consequently

be better positioned to

attract
more
students
.

Major and Minor program in Statistics
already exist.
These programs will offer a subset of the common core of the revised Specialist
pr
ogram in Statistics.


The
revised
program will consist of two streams
: Quantitative Finance, which

corresponds to the
existing Mathematical Finance, Management and Econ
o
mics stream; and Machine Learning and
Data Mining, which
is an exciting and innovative
new area of study
on
the
cutting
-
edge area of
statistics.



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Type A: Significant Modification to
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The Quantitative Finance stream will serve the type of student attracted to the
Mathematical
Finance, Management and Economics stream of the existing Specialist in Quantitative Analysis.
.
Th
is

re
vised and renamed
stream has been substantially enriched in comparison to the
existing
stream
, and modified to better serve the needs of students interested in this subject. In particular,
the existing
Mathematical Finance, Management and Economics
stream
has only two courses
that
can
properly be said to be concerned with quantitative finance. The
revised Quantitative
Finance

stream
, however,
will have four such courses
:

These courses

will provide students with a
deeper understanding of theoretical concepts
,

and more practice in their application. The depth of
this stream with respect to its requirements in computing, mathematics, statistics and quantitative
finance will substantially enhance its attractiveness to prospective students in this area. This
stre
am
differs from similar
streams within existing programs in Statistics
offered at U of T and
other institutions as it provides more of the mathematical, statistical, and computer science
background that underlies the concepts of quantitative finance.



The Statistical M
achine Learning and Data Mining stream

offers a coherent and sustained focus
on

an exciting new area
of

statistics that has attract
ed

significant attention
.

(
See, for example, the
following

articles
:

Big Data’s Impact in the World, New Yor
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Fernando Pereira,

Research Director
, Google


From:
Fernando Pereira <pereira@google.com>

Date:
April 13, 2012 1:02:13 PM EDT

To:
Ruslan Salakhutdinov <rsalakhu@cs.toronto.edu>

Cc:
vassos@cs.toronto.edu


Dear Ruslan and Vasso
s
,


I'm writing in support of your proposed specialist program in statistical machine learning and data mining.
Most of my team at Google are research scientists and software engineers who specialized in machine
learning or at least have a solid background in
the field. More broadly, all product areas at Google use
machine learning in critical ways, and employ many software engineers
--

and are always looking for
more
--

with training in the field, not just at PhD level but also masters and bachelor level.

Mach
ine


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learning is critical to our ability to present users with the most relevant content, and curb spam and other
service abuse. Increased education in the field is of great value not only to Google but to all of its
competitors and to most new information
technology companies, who need to analyze and make
predictions from increasing masses of diverse data.


--

Fernando Pereira

Research Director, Google, Mountain View


Kishore Papineni, Senior Director of Research and Senior Principal Scientist, Yahoo


From:
Kishore Papineni <kpapi@yahoo
-
inc.com>

Subject:
Machine Learning specialist program

Date:
April 13, 2012 10:27:35 PM EDT

To:
Ruslan Salakhutdinov <rsalakhu@cs.toronto.edu>

Cc:
vassos@cs.toronto.edu


Hi
Russ,


I am excited to hear that you are putting together a new undergraduate program for machine
learning.

This cannot come at a better time!

Right now, machine learning is
white hot

in the industry
which

is seeing an explosion of data from various sources.

For internet and e
-
commerce companies like
Amazon, e
-
Bay, Google, Yahoo, exploiting web
-
scale data has become a key strategic goal in the recent
years.

“Big data” (and machine learning) has entered th
e vernacular of chief executives, of late:


Yahoo’s CEO:

http://
www.adweek.com/news/technology/yahoo
-
ceo
-
s
-
plans
-
coincide
-
google
-
s
-
privacy
-
policy
-
change
-
137728


AOL’s CEO: “"We have a very big advantage both in the ad network and exchange spa
ces because of
the machine
-
learning we have with
Advertising.com
, and the ad format work we’ve been doing,"
Armstrong said during the press call.

(
http://
www.adexchanger.com/ad
-
exchange
-
news/aols
-
arms
trong
-
well
-
get
-
deeper
-
into
-
the
-
exchange
-
space
-
in
-
2012/ )


Yahoo’s former CEO:

http://
techcrunch.com/2010/11/16/bartz
-
yahoo
-
numbers/


The explosion of data coupled and cheap computing are the primary drivers of supercharged demand for
people
with skills to analyze gobs of data and extract abstractions for delivering personalized services (eg
which

news story to show to a Yahoo! visitor) and derive competitive advantage for the
business.

Yahoo! has one of the best machine learning groups in t
he world (and I am fortunate to lead it),
and the reason for our existence is that machine learning is key to Yahoo!’s future.



A testament to the demand for people in this area:

for reasons I cannot go into, the junior
-
most member
of my team was recent
ly looking for jobs.

Within three weeks of his job search, he has had five written
offers on hand from famous internet brands and was able to tell the world’s largest e
-
commerce company
that he was not interested in them!



There are simply not enough pe
ople on the job market today to satisfy the needs of companies that look
to unlock the value in their data.

I totally welcome your initiative to start a new program in this

area.

I
wish more universities have done this five years ago, but it is never to
o late.

I hope you will provide a
steady stream of well
-
trained students to the industry as well as into graduate programs in this field.


Best,

-

kishore



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Chris Burges, Principal Researcher and Manager of the Machine Learning and
Intelligence Lab,
Microsoft Research


From:
Chris J.C. Burges <Chris.Burges@microsoft.com>

Subject:
On the Importance of Machine Learning

Date:
April 20, 2012 6:44:50 PM EDT

To:
Ruslan Salakhutdinov <rsalakhu@cs.toronto.edu>

Cc:
vassos@cs.toronto.edu


Hi Russ



Here’s my
paragraph.


I hope it helps!


Cheers


--

Chris


To Whom It May Concern:


Russ asked me to outline how important machine learning is to Microsoft.


My answer is simple and
unqualified: it is extremely important.


It is now recognized throughout the company
, and in particular at
the highest levels (Steve Ballmer often mentions machine learning by name, when he talks about the
future of Microsoft), that machine learning is playing, and will continue to play, a key role in the growth of
high technology informa
tion companies.


Many product groups now specifically aim to hire graduates with
machine learning expertise.


In addition, Microsoft Research has machine learning groups at all the major
labs (Redmond, Beijing, New England, Cambridge, and Silicon Valley).


In summary, I believe that
students graduating with expertise in machine learning and data mining will continue to be attractive
potential employees to Microsoft in the future.


Please note that these are my opinions and that they are not endorsed by Micr
osoft.



3.

Requirements

.

Existing
Online
Calendar Entry
:


SPECIALIST PROGRAM IN QUANTITATIVE ANALYSIS (SCIENCE)

Supervisor of Studies:

M. Evans
Email:

evans@utsc.utoronto.ca


The Program in Quantitative Analysis is an interdisciplinary program designed for students
interested in applying mathematical ideas and analysis to problems in the biological sciences,
social and health sciences, physical sciences, and in finance and risk

management. After
completing this program students will be well prepared to pursue professional careers as
quantitative analysts, go on to professional masters programs in such areas of application or to
pursue research degrees in the areas in these field
s that require a strong training in quantitative
methods.


The program requires 13.0 credits in total. Students will be required to complete a culminating
project course in their final year of studies that applies the computational, mathematical, or
statis
tical skills they have acquired. It is strongly recommended that they complete the equivalent


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of a minor in an area of application. Suggested areas are: Mathematical Finance, Biological
Sciences, Physical Sciences, and Social and Health Sciences. The progr
am has streams
corresponding to these. Students should select an area of application in consultation with the
Supervisor of Studies. For the project course the student needs a supervisor in the appropriate
department, also selected in consultation with the

Supervisor of Studies.


The Specialist Program in Quantitative Analysis is eligible for inclusion in the Co
-
operative
Program in Physical Sciences and in the Concurrent Teacher Education Program (CTEP). Please
refer to the
Physical Sciences

section, the
Co
-
operative Programs

section and t
he
Concurrent
Teacher Education

section of this
Calendar

for further information.


Program Requirements

This program requires 13.0 credits
including at least 4.0 credits at the C
-

or D
-
level of which at
least 1.0 must be at the D
-
level.

Writing requirement

(0.5 credits)

(Should be completed by the end of second year.)

One of:

ANTA01H3
,
ANTA02H3
, (CLAA02H3), (CTLA19H3),
CTLA01H3
,
ENGA10H3
,
ENGA11H3
,
ENGB06H3
,
ENGB07H3
,
ENGB08H3
,
ENGB09H3
,
ENGB17H3
,
ENGB19H3
,
ENGB50H3
,
ENGB51H3
,
GGRA02H3
,
GGRA03H3
,
GGRB05H3
, (GGRB06H3),
(HISA01H3),
HLTA01H3
,
HUMA01H3
, (HUMA11H3), (HUMA17H3), (LGGA99H3),
LINA01H3
,
PHLA10H3
,
PHLA11H3
,
WSTA01H3
.


First Year

(3.0 credits specified)

CSCA08H3

Introduction to Computer Programming

CSCA48H3

Introduction to Computer Science

CSCA67H3

Discrete Mathematics for Computer Scientists

MATA23H3

Linear Algebra

I

MATA31H3

Calculus I for Mathematical Sciences

MATA37H3

Calculus II for Mathematical Sciences


Second Year

(4.0 credits specified)

CSCB07H3

Software Design

CSCB36H3

Introduction to the Theory of Computation

CSCB
63H3

Design and Analysis of Data Structures

MATB24H3

Linear Algebra II

MATB41H3

Techniques of the Calculus of Several Variables I

MATB44H3

Differential Equations I

STAB52H3

Introduction to Probability

STAB57H3

Introduction to Statistics


Second, Third and Fourth Years

Students should choose a stream during their second year of studies which fits with the area of
application that interests them.





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Biological and Life Sciences Stream

(5.0 credits)

CSCC43H3

Introduction to Databases

CSCD11H
3

Machine Learning and Data Mining

MATB42H3

Techniques of the Calculus of Several Variables II

[
MATB61H3

Linear Programming and Optimization
or

CSCC73H3

Algorithm Design and
Analysis]

MATC46H3

Differential Equations II

STAC52H
3

Experimental Design

STAC62H3

Stochastic Processes

STAC67H3

Regression Analysis

STAD37H3

Multivariate Analysis

Plus 0.5 additional full credits from ACT, CSC, MAT or STA courses at the B
-
le
vel or above.


Physical Sciences Stream

(5.0 credits)

CSCC37H3

Introduction to Numerical Algorithms for Computational Mathematics

CSCD37H3

Analysis of Numerical Algorithms for Computational Mathematics

MATB42H3

Techniques of the Calculus of Several Variables II

MATB43H3

Introduction to Analysis

MATC34H3

Complex Variables

MATC35H3

Chaos, F
ractals and Dynamics

MATC46H3

Differential Equations II

STAC62H3

Stochastic Processes

Plus 1.0 additional full credit from ACT, CSC, MAT or STA courses at the B
-
level or above, of
which at least 0.5 credit must be at the D
-
level.


Mathematical Finance, Management and Economics Stream

(5.0 credi
ts)

ACTB40H3

Fundamentals of Investment and Credit

CSCC37H3

Introduction to Numerical Algorithms for Computational Mathematics

CSCD11H3

Machine Learning and Data Mining

MATB42H3

Techniques of the Calculus of Several Variables II

MATB61H3

Linear Programming and Optimization

MATC46H3

Differential Equations II

STAC62H3

Stochastic Processes

STAC67H3

Regression Analysis

STAC70H3

Statistics and Finance

STAD57H3

Time Series Analysis


Social and Health Sci
ences Stream

(5.0 credits)

CSCC37H3

Introduction to Numerical Algorithms for Computational Mathematics

CSCC43H3

Introduction to Databases

MAT
B61H3

Linear Programming and Optimization

STAC52H3

Experimental Design

STAC62H3

Stochastic Processes

STAC67H3

Regression Analysis

STAD37H3

Multivariate Analysis

STAD57H3

Time Series Analysis



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Plus 1.0 additional full credits from ACT,

CSC, MAT or STA courses at the B
-
level or above.


Fourth year

(0.5 credits)

One of:

CSCD94H3

Computer Science Project

MATD94H3

Mathematics Project

STAD94H3

Statistics Project



Proposed
Calendar Entry
:


SPECIALIST PROGRAM IN STATISTICS (SCIENCE)

Supervisor of Studies
: Sotirios Damouras (416
-
287
-
7679)
Email
:
sdamouras@utsc.utoronto.ca


Program Objectives

This program provides training in the discipline of Statistics. Students are given a
thorough
grounding in the theory underlying statistical reasoning and learn the methodologies associated
with current applications. A full set of courses on the theory and methodology of the discipline
represent the core of the program. In addition student
s select one of two streams each of which
provides immediately useful, job
-
related skills. The program also prepares students for further
study in Statistics and related fields.


The

Quantitative Finance Stream

focuses on teaching the computational, mathem
atical and
statistical techniques associated with modern day fin
ance. Students acquire a thorough
understanding of the mathematical models that underlie financial modeling and the ability to
implement these models in practical settings. This stream prepare
s students to work as
quantitative analysts
in the financial industry, and for further study in Quantitative Finance.


The

Statistical Machine Learning and Data Mining Stream
focuses on applications of
statistical theory and concepts to the discovery (or “
learning”) of patterns in massive data sets.
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Program Requirements

To complete the program, a student must meet the course requirements described below. (One

credit is equivalent to two courses.)


The first year requirements of the two streams are almost identical, except that the Quantitative
Finance stream requires ECMA04H3 while the Statistic
al

Machine Learning and Data Mining
stream requires CSCA67H; these

courses need not be taken in
the
first year. In
the
second year
the two streams have considerable overlap. This structure makes it relatively easy for students to
switch between the two streams as their interests in
S
tatistics become better defined.

No
te: There are courses on the St. George campus that can be taken to satisfy some of the
requirements of the program. STAB52H3, STAB57H3 and STAC67H3, however, must be taken


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at the University of Toronto Scarborough; no substitutes are permitted without perm
ission of the
program supervisor.


CORE (7.5 credits)


1. Writing Requirement

(0.5 credit) *

One of:

ANTA01H3, ANTA02H3, (CLAA02H3), (CTLA19H3), CTLA01H3, ENGA10H3,
ENGA11H3, ENGB06H3, ENGB07H3, ENGB08H3, ENGB09H3, ENGB17H3, ENGB19H3,
ENGB50H3, ENGB51H3, G
GRA02H3, GGRA03H3, GGRB05H3, (GGRB06H3), (HISA01H3)
HLTA01H3,

HUMA01, (HUMA11H3), (HUMA17H3), (LGGA99H3), LINA01H3,
PHLA10H3, PHLA11H3, WSTA01H3.

(*) It is recommended that this requirement be satisfied by the end of the second year.


2. A
-
level courses

(2.5 credits)

CSCA08H3 Introduction to Computer Programming

CSCA48H3 Introduction to Computer Science

MATA23H3 Linear Algebra I

One of:

MATA31H3* Calculus I for Mathematical Sciences

MATA30H3 Calculus I for Biological or Physical Sciences

One of:

MATA37H3
* Calculus II for Mathematical Sciences

MATA36H Calculus II for Physical Sciences

(*) MATA31H3 and MATA37H3 are recommended; the latter requires the former.


3. B
-
level courses

(2.5 credits)

MATB24H3 Linear Algebra II

MATB41H3 Techniques of the Calculus of

Several Variables I

MATB61H3 Linear Programming and Optimization

STAB52H3 Introduction to Probability

STAB57H3 Introduction to Statistics


4. C
-
level courses

(1.5 credits)

CSCC37H3 Introduction to Numerical Algorithms for Computational Mathematics

STAC62H3 Stochastic Processes

STAC67H3 Regression Analysis


5. D
-
level course

(0.5 credits)

STAD37H3 Multivariate Analysis



A. Quantitative Finance Stream

This stream requires a total of 26 courses (13 credits). In addition to the core requirements, 11
ot
her courses (5.5 credits) must be taken
satisfying all of the following requirements:





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6. Additional A
-
level course

(0.5 credit)

ECMA04H3 Introduction to Microeconomics: A Mathematical Approach


7. Additional B
-
level courses

(2
.0

credits)

ACTB40H3 Fundam
entals of Investment and Credit

MATB42H3 Techniques of Calculus of Several Variables II

MATB44H3 Differential Equations I

STAB41H3 Financial Derivatives


8. Additional upper
-
level courses

(3
.0

credits)

MATC46H3 Differential Equations II

STAC70H3 Statistics

and Finance I

STAD57H3 Time Series Analysis

STAD70H3 Statistics and Finance II

Two of:

APM462H1

Nonlinear Optimization

CSCC11H3 Machine Learning and Data Mining

MATC37H3 Introduction to Real Analysis

STAC51H3 Categorical Data Analysis

STAC58H3 Statistical

Inference*

STAC63H3 Probability Models

STAD68H3 Advanced Machine Learning and Data Mining


STAD94H3
Statistics Project

(*) Especially recommended for students planning to pursue graduate study in statistics


B. Statistical Machine Learning and Data Mining

Stream

This stream requires a total of 26 courses (13 credits). In addition to the core requirements 11
other courses (5.5 credits) must be taken satisfying all of the following requirements:


6. Additional A
-
level course

(0.5 credit)


CSCA67H3 Discrete
Mathematics for Computer Scientists


7. Additional B
-
level courses

(1
.0

credit)

Two of:

CSCB07H3 Software Design

CSCB20H3 Introduction to Databases and Web Applications

CSCB36H3 Introduction to the Theory of Computation

CSCB63H3 Design and Analysis of Data

Structures


8. Additional upper
-
level courses

(4.
0

credits)

CSCC11H3 Machine Learning and Data Mining

STAC58H3 Statistical Inference

STAD68H3 Advanced Machine Learning and Data Mining

Five of: *

C or D
-
level CSC, MAT or STA courses (excluding STAD29H3), t
hree of which must be
STA courses.



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(*) Some of the courses on this list have prerequisites that are not included in this program; in
choosing courses to satisfy this requirement, check the prerequisites carefully and plan
accordingly.


SPECIALIST (CO
-
OPER
ATIVE) PROGRAM IN STATISTICS

Supervisor of Studies
: Sotirios Damouras (416
-
287
-
7679)
Email
:
sdamouras@utsc.utoronto.ca

Co
-
op Contact: askcoop@utsc.utoronto.ca


Program Objectives

This program combines the coursework of the Specialist Program in Statistics
described above
with paid work terms in

public and private enterprises. It shares the goals and structure of the
Specialist Program in Statistics, including its two streams (Quantitative Finance and Statistical
Machine Learning and Data Mining), but comple
ments study of the subject with considerable
work experience.


Program Admission

Refer to the Program Admission requirements for the Co
-
operative Programs section in this
Calendar
.


Program Requirements

To remain in the program, a student must maintain
a cumulative GPA of 2.5 or higher
throughout the program. To complete the pr
ogram, a student must meet the work term and
course requirements described below.



Work Term Requirements

Students must successfully complete three work terms,
only

one of which
can be during the
summer. In addition, prior to their first work term, students must successfully complete the Arts
& Science Co
-
op Work Term Preparation Activities. These include networking sessions, speaker
panels and industry tours along with seminars
covering resumes, cover letters, job interviews and
work term expectations.


Course Requirements

The Co
-
operative Program can be taken in conjunction with any of the streams in the Specialist
Program in Statistics. For the course requirements of each stre
am, please refer to the description
of the Specialist Program in Statistics.



4.

Impact of the Change on Students


Students will be allowed to complete the current program but it is very likely that most will elect
to switch to the Quantitative Finance

stream of the Specialist Program in Statistics
. There will be
no deadline for students to complete the
discontinued streams

and inactive students will be able
to complete the program.


Given the very low demand for streams other than
Mathematical
Finance
, Management and
Economics

in this program we do not believe there will be any restrictions in academic options


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when this program is
modified
.


We also expect to attract a number of students to the new Machine Learning and Data Mining
stream.



5.

Consultat
ion

.

The
changes to

this program involved extensive discussions within the
Statistics
group with the
participation of all
Statistics
faculty and the
Department’s
chair, and consultations with
interested faculty in
Computer Science
. In addition
we have con
sulted

with

the

Department of
Management (Chair and faculty)
about the

Quantitative Finance stream. The
proposed
form of
the Quantitative Finance stream was shaped to address
any
concerns expressed by our
Management colleagues
---

email communication to that effect
is
included below.


We do not believe that this program will have any impact on other departments.


Date: Wed, 28 Mar 2012 12:37:06
-
0400

From: David Zweig <mgmtchair@utsc.utoronto.ca>

To: 'Vassos Hadzilacos' <vassos@cs.toronto.edu>

Cc: 'Rick Hal
pern' <vpdean@utsc.utoronto.ca>,


'John Scherk' <vicedean@utsc.utoronto.ca>,


'Mike Evans' <mevans@utstat.utoronto.ca>,


'Sotiris Damouras'

<sotirios.damouras@utoronto.ca>

Subject: RE: Proposed CMS program in Quantitative Finance


Hi Vassos:


Thank you again for addressing our concerns. We do think it adds more

clarity to remove the term "Finance"
from
the specialist program name so

that students do not become confused between the offerings and focus of our

respective department's programs. Thank you for your consideration with

this. The finance faculty did caution me
that students who start their

c
areers in analyst positions will eventually enter into management or

consulting roles
where they will be required to engage in the application of

mathematical principles. This is where more knowledge
of management topics

would help. If the students stream
into actuarial positions, this is less of

a concern.
Nevertheless, it is

something to be aware of and it might be

worth clarifying this for students.


The faculty also pointed out to me that replacing STAB41 with MGTC71 course

would require opening up all
of the
prerequisite courses in Management,

which would be problematic. That being said, we are still open to discussing

a
shared program should you want to expand the focus of your program in the

future to include more management
content. We could develop
a very strong

program together. In the meantime, with the adoption of a specialist
program

name that avoids overlap, and clear messaging that differentiates the focus

and outcomes of our specialist
streams, we wish you the best of luck with

the new program
.


Best,

Dave



David Zweig, Ph.D

Associate Professor and Chair

Department of Management




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6.

Resources:


No new faculty are needed to deliver
the revised
program. It was designed to be sustainable with
our current
statistics faculty complement.


A number of new courses are proposed as
part of the

revisions proposed
. The TA support
implications of these courses are described in the new course proposal forms submitted alongside
this proposal. The overall picture is that only increases to the TA bu
dget of the department
justified by increased enrolments will be required.


There are no additional space, library, or enrolment/admissions requirements for this program
change.


7.

Governance Process:













Develop
ed

by the Office of the Vice
-
Provost, Academic Programs: April 4, 2011

Revised by the Office of the Dean and VP (Academic): 24 Feb
ruary, 2012





Levels of Approval Required

Date

Departmental
Curriculum Committee

May 11, 2012

Dean’s Office Sign Off

June 5, 2012

UTSC Divisional Governance


Submission to Provost’s Office


Report to
AP&P


Report to
Ontario Quality Counci
l