Lecture 1

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CIT 592
Discrete Math
Lecture 1
By way of introduction …
• Arvind Bhusnurmath
• There are no bonus points for pronouncing my last name
correctly
• Please call me Arvind
• New to teaching at Penn
• However, not new to Penn. Graduated 2008
• https://www.grasp.upenn.edu/
• http://www.youtube.com/watch?v=4ErEBkj_3PY (my fav TED talk)
• Worked 5 years at http://www.predictivetechnologies.com/
Who is this course for?
• MCIT students
• Objective : To learn fundamentals of discrete math in
order to understand algorithm complexity, proving a
program works, graph theory etc
Textbook and resources
•
•
•
Textbook – Discrete Mathematics for Computer Scientists by
Stein et al.
Intend to follow textbook fairly closely but might skip some parts
Resources
•
•
•
Discrete Mathematics by Rosen et al. (a more mathematical
treatment of the topic)
Elements of discrete mathematics by Liu et al. (a classic)
Mathematics: A discrete introduction – Scheinerman
Logistics
• Main website for announcements/readings
http://www.seas.upenn.edu/~bhusnur4/cit592/
• Office hours and recitations
• My office ´â│Moore/Levine/GRW 268. Opposite Weiss
Tech house.
Schedule (might change…)
• Counting principles – Chapter 1
• Proof techniques – Chapter 3 + some additional material
• Induction – Chapter 4
• Probability – Chapter 5
• Graph Theory – Chapter 6
• Time permitting we will cover number theory and RSA
encryption – Chapter 2
Exams, homeworks etc
• Homework handed out/posted every Thursday
• Due next Thursday at the start of class.
• Deadlines will be strictly enforced
• If you do have an emergency, send me email or have one of your
classmates send me email
• Grading
• 2 midterms 20% each
• 1 final 30%
• Homeworks 30%
How to turn in assignments
• Print out/handwritten assignments
• Word/Latex submissions encouraged
• If you are submitting handwritten solutions please write
neatly
• The rule of ‘If I can’t read it, you haven’t written it’ will be applied
Why should I do this math stuff???
Why should I do this course
• Discrete math teaches mathematical reasoning and proof
techniques.
• The mathematics of modern computer science is built
almost entirely on discrete math, in particular
combinatorics and graph theory
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