As defined P. Denning et al. in an 1989 ACM report on
Computing as a Discipline, computing is the
"systematic study of algorithmic processes that describe and
transform information: their theory, analysis, design,
efficiency, implementation, and application. The fundamental
question underlying all computing is what can be
(efficiently) automated?". This course precisely aims at
introducing the science (and the art) of computing to students
with little or no prior background in the matter.
Given the inherent vastity of the topics involved in
computing, the course will focus on a subset of the core
aspects of computing, providing a brief, yet substantial
introduction to many topics. The goal is to provide an idea of
what's possible and how to realize it when it's useful (or,
most often, necessary) to employ computation and computers to
accomplish a complex goal.
The course will balance the discussion of concepts central to
design and understand computation with their practical
implementation and testing using a specific programming
language, Python. The choice of python is motivated
by its widespread use, relative simplicity, availability of
tools for scientific, graphical, and web programming, among
others.
The course will take the student along the way that starts
from a complex, possibly large problem to solve, and then move
step by step to its abstraction, to its formalization into an
algorithmic recipe, to the encoding of the algorithm using the
constructs of the python language, to the run-time execution
and error correction of the programming code, to the
efficiency analysis of the developed algorithm and
code. A number of practical application scenarios will be
considered to ground the concepts to problems of interest.
In a nutshell, the course will tackle the following topics:
Problem analysis: abstraction and representation
Concepts central to design and understand algorithms
Fundamental concepts of programming:
Knowledge representation (data types and structures)
Flow control (conditionals, iteration, recursion)
Data organization (lists, matrices, dictionaries)
Decomposition, reusability, information hiding (modules, functions, class objects)
Python programming language to implement the above general concepts
Correctness and efficiency of algorithms (testing, error finding, algorithmic complexity, optimization)
Data visualization, animation, and statistical analysis
Simulation models (deterministic and stochastic approaches)
While the theoretical understanding of the concepts is always
fundamental, the course emphasizes hands-on, meaning
that learning will be mostly driven by the repeated
implementation and application in python of the general
concepts. Weekly lab classes and programming homework will
support this way of proceeding. In the final three weeks, a
project work will be devoloped by the students, that will
allow to face the challenge of designing and implementing a
relatively large and complex code.
Learning goals
To achieve a broad understanding and a practical expertise of what computing is, and, more
in general, of what computer science is.
To develop the knowledge of what classes of problems computers are good at or are necessary
for.
To build the ability to analyze and abstract a given problem to represent it in a way which is ameneable
to an algorithmic solution.
To acquire the skills to step from a problem description, to a problem abstraction, to
an effective algorithm, to an optimized computer program.
To acquire the basic knowledge of some fundamental algorithmic solution techniques.
To reach a proficiency level programming in Python using both
procedural and object-oriented techniques.
To become a skilled practioner for using computer programming to read, write, and process
complex data.
To achieve a clear understanding of the time and memory requirements of a specific
algorithm, and, more in general, of the inherent limits of what a specific computer can solve.
To know how to effectively use python programming for general-purpose tasks such as
data visualization and design and execution of simulated experiments.
Prerequisites
None.
Credits
This is a 10 unit course.
Place and time
Sundays and Tuesdays
13:30 to 14:50
2052
lectures
Thursdays
13:30 to 14:50
2062
labs
Schedule (Subject to changes)
In the table below, labs are in blue, exams are in red and the next
lecture/lab is orange. Green days are in the past, and grey days have no lectures.
Staff
Gianni A. Di Caro (Teacher)
Office: 1007
Office hours: by appointment
E-mail: gdicaro@cmu.edu
Giselle Reis (Teacher)
Office: 1008
Office hours: by appointment
E-mail: giselle@cmu.edu
The writeup and starter files for each assignment can be found on
Autolab.
Log in with your andrewID, select the course and then the assignment.
You can see the pdf with the instructions by clicking on
"View writeup".
The starter code and template for typing your answers are in a
zip file which can be downloaded by clicking on "Download
handout".
Uncompressing this file will create the directory
<asgn>-handout with the starter code.
If you need to download these files again, be
careful not to overwrite your work!
Submitting the Assignments
You need to submit a zip file containing the completed
started code.
The zip file needs to be submitted to
Autolab.
It will immediately run a series of tests on your code and give you a
score with some feedback. Make sure to check it! If your code does not
pass all tests, you can submit it again until the deadline. You are limited to a total of 10 submissions
for each assignment.
Tools
Set up your your programming environment by installing
the Anaconda distribution (Pyhton 3.7 version):
The download is big, so make sure you are at a place with fast and
reliable internet connection.
Once installed, you can start the Anaconda-Navigator and launch from there
the Spyder editor, which you will use to develop and test your python code.
Collaboration is regulated by the whiteboard policy: you can bounce
ideas about a homework with other students, but when it comes to typing
it down for submission, you are on your own. You are not allowed to use
notes, files, pictures, etc, from any previous discussion nor previous
versions of this course.
We will run a program that detects code similarity on all your homework
submissions. If students are found to have submitted almost the same
code, they will be contacted and this may result on an academic integrity
violation report.
An Invitation to Students with Learning Disabilities:
Carnegie Mellon University is committed to providing reasonable
accommodations for all persons with disabilities. To access
accommodation services you are expected to initiate the request and
submit a Voluntary Disclosure of Disability Form to the office of Health
& Wellness or CaPS-Q. In order to receive services/accommodations,
verification of a disability is required as recommended in writing by a
doctor, licensed psychologist or psycho-educational specialist. The
office of Health & Wellness, CaPS-Q and Office of Disability Resources
in Pittsburgh will review the information you provide. All information
will be considered confidential and only released to appropriate persons
on a need to know basis.
Once the accommodations have been approved, you will be issued a Summary
of Accommodations Memorandum documenting the disability and describing
the accommodation. You are responsible for providing the Memorandum to
your professors at the beginning of each semester.
Take Care of Yourself:
Do your best to maintain a healthy lifestyle this semester by eating
well, exercising, getting enough sleep and taking some time to relax.
This will help you achieve your goals and cope with stress.
All of us benefit from support during times of struggle. You are not
alone. There are many helpful resources available on campus and an
important part of the college experience is learning how to ask for
help. Asking for support sooner rather than later is often helpful.
If you or anyone you know experiences any academic stress, difficult
life events, or feelings like anxiety or depression, we strongly
encourage you to seek support. Counseling and Psychological Services
(CaPS-Q) is here to help: call 4454 8525 or make an appointment to see
the counselor by emailing
student-counselling@qatar.cmu.edu.
Consider reaching out to a friend, faculty or family member you trust
for help.
If you or someone you know is feeling suicidal or in danger of
self-harm, call someone immediately, day or night at 5554 7913. If the
situation is life threatening, call 999.
