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ALGORITHMS

ALGORITHMS. Lecturer: Daniela Zaharie Room: 047 (ground floor) http: //www.info.uvt.ro/~dzaharie e-mail: dzaharie@info.uvt.ro Schedule: Lecture: every Thursday at 14:40 (room A02) Seminars: Tuesday at 11:20, room 102 (Isabela Dramnesc, idramnesc@info.uvt.ro)

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ALGORITHMS

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  1. ALGORITHMS Lecturer: Daniela Zaharie • Room: 047 (ground floor) • http: //www.info.uvt.ro/~dzaharie • e-mail: dzaharie@info.uvt.ro • Schedule: Lecture: every Thursday at 14:40 (room A02) Seminars:Tuesday at 11:20, room 102 (Isabela Dramnesc, idramnesc@info.uvt.ro) • Group 1 (odd weeks) • Group 2 (even weeks) Lab: Tuesday (Marian Neagul, marian@info.uvt.ro) • 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

  2. 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

  3. 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: rank(P0)=(1-d)+d*(rank(P1)+…rank(Pk)) 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

  4. 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: [http://www.facebook.com/careers/fellowship.php]: • “Algorithmic game theory” • “Algorithms around social networks” • “Search algorithms” Algorithms – Lecture 1

  5. 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 Pythonhttp://www.python.org) • 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

  6. 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 Algorithms - Lecture 1

  7. 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

  8. 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

  9. Syllabus 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

  10. Course materials Web page: http://www.info.uvt.ro/~dzaharie 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

  11. 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

  12. 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

  13. 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

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