STATISTICAL LEARNING 1. Introduction and Administration

Welcome  “Statistical Learning”  Tue 14:15-16:45  Room 031  Instructor: Sasha Apartsin   Course Web Page   Slides, References etc.

Prerequisites  Linear Algebra  Matrices  Vector Spaces  Basic Probability (Must!)  Random Variables  Distribution Functions  Conditional Distribution  Expectation and Variance

What is Machine Learning? Based on E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 4  Need an algorithm to solve a problem on a computer  An algorithm is a sequence of instructions to transform input from output  Example: Sort list of numbers  Input: set of numbers  Output: ordered list of numbers  Many algorithms for the same task  May be interested in finding the most efficient

What is Machine Learning? Based on E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 5  Don’t have an algorithm for some tasks  Example: Tell the spam for legitimate s  Know the input (an ) and output (yes/no)  Don’t know how to transform input to output  Definition of spam may change over the time and from individual to individual  We don’t have a knowledge, replace it with data  Can easily produce large amount of examples  Want a computer to extract an algorithm from the examples

Course Structure  First half  Presentation of key concept and techniques  Hopefully guest lecturers from the industry on use of Machine Learning for real-life problems  Slides will be available on course web page  Second half  Student presentations of various applications of machine learning (e.g. face recognition, speech recognition, OCR, recommendation systems etc.)  List of recommended subjects/references will be published soon

Course Grade  45%: Extended summary of the subjects presented during the first half of the course  In groups of 3  16 chapters of the textbook=>16 summaries  In Hebrew, no cut and paste  Concise, informative, self-contained clean presentation  Submission deadline: day of the last  Send by in word format  Same grade for each member of the group  Bid for a chapter starting today

Course Grade-Cont’d  45% :Student presentation of advanced subjects  In groups of 3  20 minutes for presentation + 10 minutes for Q&A  Clean, concise, informative  Every member of the group should talk  List of recommended subjects/papers/references and instructions will be published soon  Submit( ) PPT after the lecture  Efficient usage of presentation time is a major grading factor  Individual Grades  Bid for a time slot (last 5 lectures) starting from today  10% : presence and participation during the second half of the course.

Some Random Points  Balance between technical depth and important concepts/ideas  Some math/technical details is inevitable  Single 15 minutes break at 18:30