Algorithms - Lecture 11 ALGORITHMS Lecturer: Daniela Zaharie Room: 047 (ground floor) http: // Schedule: Lecture: every Monday at 13:00 (room 045C) Seminar:Tuesday at 9:40, room 102 (Flavia Micota, Lab: (Flavia Micota, Subgroup 1: Monday, odd weeks, 9:40h, room 031 Subgroup 2: Tuesday, odd weeks, 8:00h, room F45 Subgroup 3: Tuesday, even weeks, 8:00h, room F45

Algorithms - Lecture 12 What is this course about ? In our daily activity we : –Use a search engine (e.g. Google) –An 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 13 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(P 0 )=(1-d)+d*(rank(P 1 )+…rank(P k )) P 0 – current page P 1,…, P k – pages which contain links toward P 0 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)

What is this course about ? Algorithms – Lecture 14 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 (between the user and object creator), weight, age. As a edge is more important the probability to be included in News is higher. facebook-edgerank/ YIYInou0 Some of the research topics at Facebook : -“Algorithmic game theory” -“Algorithms around social networks” -“Search algorithms”

Algorithms - Lecture 15 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; some mathematical logic, proof techniques like mathematical induction or proof by contradiction)

Algorithms - Lecture 16 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 17 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 18 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 19 Syllabus Fourteen lectures on: 1. Introduction to algorithmic problem solving 2. Description of 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 110 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 111 Course materials The course materials are based mainly on: 1.T.H. Cormen, C.E. Leiserson, R.R. Rivest - Introduction to algorithms, MIT Press R. Johnsonbaugh, M. Schaefer - Algorithms, Pearson Education, 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 112 Grading policy The final grade is between 1 and 10 and is based on: Midterm written test - 20% (during the 4 th seminar – weeks 7-8) Lab practical test – 20% (during the 5 th lab – weeks 9-10) Homework & seminar/lab/course activity - 15% Final written and practical exam - 45% (during the winter exam session)

Algorithms - Lecture 113 Some rules The homework should be finalized by the next seminar/lab; late homework is penalized with 0.2 points/week More than 2 absences at seminar/lab lead to exam failing 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...