stat340s13

Introduction

Four Fundamental Problems:

1. Classification: Given an input object X, we have a function which will take in this input X and identity which 'class (Y)' it belongs to. (Discrete Case) 2.Regression: Same as classification but in the continuous case 3.Clustering: Use common features of objects in same class or group to form clusters. 4. Dimensionality Reduction

Applications

Most useful when structure of the task is not well understood but can be characterized by a dataset with strong statistical regularity Examples Computer Vision, Computer Graphics, Finance (fraud detection), Machine Learning Search and recommendation (eg. Google) Automatic speech recognition, speaker verification Text parsing Face identification Tracking objects in video Financial prediction, fraud detection

Course Information

No required textbook, recommended: "Simulation" by Sheldon M. Ross Computing parts of the course will be done in Matlab, but prior knowledge of Matlab is not essential (will have a tutorial on it) Learn for announcements, assignments, and emails. Other course material on: <<wikicoursenote.com>> Log on to both Learn and wikicoursenote frequently.

Must do both Two types: primary contributor: 1 lecture during the whole semester, must put a summary of the lecture up within 48 hours. General contributor: elaborate on concepts, add example, add code, add pictures, reference, etc… responsible for 50% of lectures. A general contribution could be correcting mistakes, etc… or technical in examples, etc… At least half of your contributions should be technical. Make contribution no later than 2 weeks after the lecture. Do not submit copyrighted work without permission, cite original sources. Each time you make a contribution, check mark the table. (Marks calculated on honour system, although everything can be verified through the history [randomly verified])

Tentative Marking Scheme

Item	Value
Assignments (~6)	30%
WikiCourseNote	10%
Midterm	20%
Final	40%