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ALGORITHMS. Lecturer: Daniela Zaharie Room: 047 (ground floor) http: // e-mail: [email protected] Schedule: Lecture: every Thursday at 14:40 (room A02) Seminars: Tuesday at 11:20, room 102 (Isabela Dramnesc, [email protected])

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Lecturer: Daniela Zaharie

  • Room: 047 (ground floor)
  • http: //
  • e-mail: [email protected]
  • Schedule:

Lecture: every Thursday at 14:40 (room A02)

Seminars:Tuesday at 11:20, room 102

(Isabela Dramnesc, [email protected])

      • Group 1 (odd weeks)
      • Group 2 (even weeks)

Lab: Tuesday

(Marian Neagul, [email protected])

      • Subgroup 1: odd weeks, 9:40h, room 032
      • Subgroup 2: even weeks, 9:40h, room 032
      • Subgroup 3: oddweeks, 14:40h, room 032

Algorithms - Lecture 1

what is this course about
What is this course about ?
  • In our daily activity we :
    • Use a search engine (e.g. Google)
    • An e-mail application which (hopefully) has a anti-spam filter
    • Find news about friends using a social network tool (e.g. Facebook)
  • What is behind these tools?
    • Algorithms for searching, keywords matching, sorting, frequency computation, correlations identification etc.

Examples: PageRank algorithm (Google), EdgeRank algorithm (Facebook)

Algorithms - Lecture 1

what is this course about1
What is this course about ?

PageRank – algorithm used by the Google search engine to rank the web pages [Larry Page, 1997]

Basic idea of ranking:


P0 – current page

P1,…, Pk – pages which contain links toward P0

d in (0,1) – damping factor (models the influence of time)

Web = graph

Ranking criteria = probabilistic scores

Rank computing = iterative algorithm or algebraic compution (solving as linear system)

Algorithms - Lecture 1

what is this course about2
What is this course about ?
  • EdgeRank – algorithm used in Facebook news feed (selection of news to be posted on the wall of a user)
  • Basic idea:
  • The interaction between a user and a facebook “object” (e.g. info, comment etc) defines an edge
  • Each edge is characterized by 3 factors which influence the importance of each edge: affinity, weight, age.
  • As a edge is more important the probability to be included in News is higher.
  • Some of the topics on which Facebook offers fellowships: []:
  • “Algorithmic game theory”
  • “Algorithms around social networks”
  • “Search algorithms”

Algorithms – Lecture 1

what is this course about3
What is this course about ?
  • This course is about:
    • designing and analyzing algorithms
    • abstract thinking and solving problems
  • This course is NOT:
    • a programming course

(however the algorithms we design and analyze will be implemented in a programming language during the labs; the language which we shall use is Python

    • a math course

(however we shall use some basic math stuff: concepts as set, function, relation; some combinatorics; proof techniques like mathematical induction or proof by contradiction)

Algorithms - Lecture 1

why such a course could be useful for you
Why such a course could be useful for you?
  • You want to become a computer scientist
  • Then you should know that:

at the heart of every programming task is the

    • selection,
    • adaptation,
    • discovery

of algorithms.

All these need a good

understanding of


Algorithms - Lecture 1

why such a course could be useful for you1
Why such a course could be useful for you?

A computer scientist must be prepared for tasks like:

” … This is the problem. Solve it ...”

In such a situation it does not suffice to know

how to code a given algorithm

You must be able to find an adequate algorithm or even

develop a new one to solve the problem

Algorithms - Lecture 1

why such a course could be useful for you2
Why such a course could be useful for you?

The future belongs to the computer scientists who have:

  • Content: An up to date grasp of fundamental problems and solutions
  • Method: Principles and techniques to solve the vast array of unfamiliar problems that arise in a rapidly changing field

[Jeff Edmonds, York University, Canada]

Algorithms - Lecture 1


Fourteen lectures on:

1. Introduction to algorithmic problem solving

2. Description of the algorithms

3. Verification of algorithm correctness

4. Analysis of algorithm efficiency

5. Sorting and searching

6. Basic techniques in algorithm design:

a) divide and conquer, decrease and conquer

b) greedy

c) dynamic programming

d) backtracking, branch and bound

Algorithms - Lecture 1

course materials
Course materials

Web page:

Algorithmics - english

There you will find some downloadable materials:

- lectures files (PDF)

- lecture slides (PPT or PDF)

- exercises for seminar/lab (PDF)

If you find typos or other errors please let me know !

Algorithms - Lecture 1

course materials1
Course materials

The course materials are based mainly on:

  • T.H. Cormen, C.E. Leiserson, R.R. Rivest - Introduction to algorithms, MIT Press 1990

2. R. Johnsonbaugh, M. Schaefer - Algorithms, Pearson Education, 2004

3. A. Levitin - The design & analysis of the algorithms, 2003

and course slides of

Jeff Edmonds (York University, Canada),

David Luebke (Virginia University, USA),

Steven Rudich (Carnegie Mellon University, USA) ...

Algorithms - Lecture 1

grading policy
Grading policy

The final grade is between 1 and 10 and is based on:

Midterm written test - 20%

(during the 4th seminar)

Practical tests – 20%

(during the 3th and 5th lab)

Homework & seminar/lab activity - 20%

Final written and practical exam - 40%

(during the winter exam session)

Algorithms - Lecture 1

some rules
Some rules
  • The homework should be finalized by the next seminar/lab; late homework is penalized with 0.2 points/week
  • At most 2 absences from seminar/lab are accepted
  • Collaboration is permitted on a conceptual level only; you can discuss with your colleagues, but written solutions must always be the result of an individual effort.Plagiarism of homework or written test is punished by not considering that homework/ test contribution to the final grade or even worse ...

Algorithms - Lecture 1