About the Major
(CS at TAMU)
Main Points
• Explain the rationale for the core course
sequence/degree plan for majors.
• Establish expectations: be prepared for
the rigor required in this major, especially
the role of mathematics
• Explain that application of computer
science is inherently multidisciplinary.
About the CS Department at TAMU
• Know your advisors (in 325 Teague)
Dr. Vivek Sarin
Dr. Richard Furuta
• Departmental organizations
–
–
–
–
AWICS – Aggie Women in Computer Science (Dr. Amato)
TACS – Texas A&M Computing Society (ACM chapter)
TAGD – Texas Aggie Game Developers (Dr. Keyser)
ACE Scholars – CSE honors program (Dr. Welch)
• Departmental seminars
– M/W, 4:10, 124 HRBB
– Watch for Distinguished Lecturers
Be Resourceful
• Degree plan, requirements, and course catalog
(pre-reqs) available on web
– BEWARE: not all courses are offered every semester
• CSCE department resources
– computer accounts, email, labs...
• TAMU resources
– library, SELL (software licenses for students)
• Be aware of expected course offerings on web
• Ask questions of...
– your peers
– the faculty
CSCE 110 – Python
CSCE 111 – Java
CSCE 206 – C
CSCE 121
Introduction to programming (C++)
CSCE 221 - Data Structures and
Algorithms
CSCE 222 - Discrete Structures
for Computing
CSCE 312
Computer Organization
CSCE 314
Programming Languages
CSCE 313
Intro to Computer Systems
CSCE 315
Programming Studio
“systems”
(or
ENGR 112+CSCE 113
for CECN)
gateway to upper level
400-level electives
CSCE 411
Analysis of Algorithms
“theory”
Other Classes
• Seminars: 181, 481
• Upper-level electives (400)
– Tracks: at least one from each
• Science classes (16 cr.)
– PHYS, CHEM, BIOL, or GEOS
• Supporting area (12 cr.)
– Could be any interest area: math, business, music....
– Must have a “computational” connection
• Capstone (CSCE 482/483)
– Team-based projects
• ENGR 482 – Engineering Ethics
Plan Your College Career!
• It takes 126 hours to graduate
– 126/8 = 15.75 hours a semester to graduate
in 4 years
– How many hours are you taking?
• What courses will you take? When will
you take those courses?
• Have a plan (it may change) or you could
easily stay in college longer than you think
Example Schedule
Role of Mathematics in Computer
Science
• Mathematical analysis plays a vital role in
many computational systems...so be
prepared for it.
• Current requirements:
– MATH 151, 152 (calc)
– MATH 304 (lin alg)
– STAT 211
– MATH 251 (vector calc) OR 302 (discrete
math) OR 304 (diff eqns)
Combinatorics
• Solving recurrence relations to estimate run-time
complexity
for (i=0 ; i<n ; i++)
for (j=0 ; j<i ; j++)
<do something>
i=0:
i=1: j=0
i=2: j=0, j=1
i=3: j=0, j=1, j=2
...
• Surface representations?
Linear Algebra
• Matrix calculations are useful for...
– Transformations in Graphics
– Scientific computing, e.g. finite element
methods
(0,0)
( w, h)
Linear Algebra
• Matrix calculations are useful for...
– Transformations in Graphics
– Scientific computing, e.g. finite element
methods
Linear Algebra
• Matrix calculations are useful for...
– Transformations in Graphics
– Scientific computing, e.g. finite element
methods
15
Fourier Transforms
•Useful for analyzing/compressing
images, sound files, speech recognition
Fourier Transforms
•Useful for analyzing/compressing
images, sound files, speech recognition
Fourier Transforms
•Useful for analyzing/compressing
images, sound files, speech recognition
Fourier
Fourier Transforms
•Useful for analyzing/compressing
images, sound files, speech recognition
Fourier
Fourier Transforms
•Useful for analyzing/compressing
images, sound files, speech recognition
Fourier
Statistics
• Compare performance of algorithms using empirical
experiments
• Evaluate performance of system under different loads
• Data analysis - finding trends, predictive models
• Distributions
– Binomial, Gamma, Poisson, Gaussian
Calculus
• Physical simulation, light transport
Calculus
• Physical simulation, light transport
The Multidisciplinary Nature of
Computer Science
• Sometimes, CS may feel like a branch of
mathematics (combinatorics, proofs of
correctness, analysis of complexity...)
• However, CS becomes important to
society when it is applied to real-world
problems
• Information Technology is pervasive
– computation, simulation, connectivity
– meteorology, health care, construction,
mapping, oil industry, pharmaceuticals,
communication, financial markets,
entertainment, even the military
• More fields are becoming ‘data-centric’
– digitization
– amassing huge databases (petabytes)
– many jobs in software industry focus on
processing/analyzing this data
– “Data Science” - databases, search, machine
learning, statistics, pattern recognition
• Each application area has its own lingo
– domain concepts, like anatomy/physiology...
– types of data, format, sources of error
• Software engineers must learn how to
communicate with customers
– to understand users’ needs in that field
– This is why we encourage you to develop an
orthogonal interest in a “supporting area”
• Since users don’t understand what things are
possible/impossible with computers, we have to
help guide the design
– if we don’t use and understand their terminology, they
won’t respect us, and nothing gets done
• More application areas:
your interest is your own, but think
about how it involves computation...
– business processes, quantitative trading
– music – MIDI format, synthetic music, sound
synthesis, filtering
– biology – genomes, metabolic pathways,
protein interactions, regulatory networks
– archeology – cataloging of artifacts,
searching, imaging, reconstruction
– automobile design – engine controllers for fuel
efficiency/power, heat, emissions,
manufacturing
– aerospace engineering - fluid dynamics and
airfoil design, air-traffic control (congestion,
routing), automation and displays in cockpit
• Another example: Interface Design (GUIs)
and Cognitive Science
– in order to design good interfaces, it helps to
understand how people think
– how do they respond to different visual inputs?
e.g. button size, color, placement
– should have a “cognitive model” of what tasks
they might be doing and what questions they
might have
– cognitive factors that impact software usage:
• attention, interference between perceptual modalities
• limits on short-term memory
• consistency – e.g. meaning of buttons like “submit”
and “cancel”
• fatigue, distraction, expectation biases...
Summary
• The course sequence focuses on learning data
structures and algorithms at the lower level
(along with systems and programming)
• Based on this foundation, you can take many
upper-level electives.
• Be prepared for the rigor required in this major.
Many topics in CS require mathematics.
• Computer science is inherently multidisciplinary,
and it is important to learn the concepts and
terminology in an application area.
Mathematics and Graphics
Graphics is mathematics made visible
Mathematics
• Calculus
• Linear Algebra
• Differential
Equations
• Real Analysis
• …
Computer Science
 Data Structures
 Searching
 Asymptotic Analysis
 Parallel Computing
…
What are Fractals?
• Recursion made visible
What are Fractals?
• Recursion made visible
What are Fractals?
• Recursion made visible
What are Fractals?
• Recursion made visible
What are Fractals?
• Recursion made visible
Rendering Fractals
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Fractal Tennis
Start with any point x
For ( i=1; i<100; i++ )
x = Trandom(x)
For ( i=1; i<100000; i++ )
draw(x)
x = Trandom(x)
Fractal Tennis
Start with any point x
For ( i=1; i<100; i++ )
x = Trandom(x)
For ( i=1; i<100000; i++ )
draw(x)
x = Trandom(x)
Gets a point on
the fractal
Fractal Tennis
Start with any point x
For ( i=1; i<100; i++ )
x = Trandom(x)
For ( i=1; i<100000; i++ )
draw(x)
x = Trandom(x)
Creates
new points
on the
fractal
Fractal Tennis – Example
25,000
Points
Fractal Tennis – Example
50,000
Points
Fractal Tennis – Example
75,000
Points
Fractal Tennis – Example
100,000
Points
Fractal Tennis – Example
125,000
Points
Fractal Tennis – Example
150,000
Points
More Fractals
More Fractals
More Fractals