cs7616 - pattern recognition - introductionhic/cs7616/pdf/lecture1.pdf · henrik i christensen...
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
CS7616 - Pattern Recognition - Introduction
Henrik I Christensen
Robotics & Intelligent Machines @ GTGeorgia Institute of Technology,
Atlanta, GA [email protected]
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Outline
1 Introduction
2 Objective / Motivation
3 Schedule / Structure
4 Homework / Exercises
5 Material ...
6 Background examples
7 Questions
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Introduction
Welcome to CS7616
Pattern Recognition
Today:
Outline of the course - Objective / MotivationSchedule of lecturesStyle of the courseExercises / ProjectsMaterial to be used in the course
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Information
Class website:http://www.cc.gatech.edu/∼hic/CS7616Schedule, Material, Slide copies, General Information
T-Square - Usual stuff, announcements, ...
Slides - PDF copy will be posted after class with summary
Piazza - You will receive an invitation for the class forumUse it for general questions / discussions
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Staffing
Henrik I ChristensenLecturer
Sidd Choudhary, TA Steven Hickson, TA Ruffin White, TA
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Outline
1 Introduction
2 Objective / Motivation
3 Schedule / Structure
4 Homework / Exercises
5 Material ...
6 Background examples
7 Questions
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Objective
Get a solid knowledge of key methods in pattern recognition
Discuss state of the art methods / techniques in pattern recognition
Explore a few representative data sets that illustrate use of patternrecognition
Explore increasingly complex methods over the semester
This is not a general machine learning course
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Motivation
PR is used everywhere in daily lives
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Speech Recognition
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Financial trading
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Medical Diagnostics
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Face Recognition
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Scene Labeling
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Netflix Movie Recommendation
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Generic Problem Structure
Feature Extraction Classification Decision
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Outline
1 Introduction
2 Objective / Motivation
3 Schedule / Structure
4 Homework / Exercises
5 Material ...
6 Background examples
7 Questions
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Structure
A mixture of foundational lectures and
Group discussions of influential/current papers
We will divide class into 4 groups for smaller discussionsEvery student is expected to present 1 paper during term as part of thegroup discussions
Large group lectures are a challenge for in-depth discussions
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Lecture topics
1 Bayes Decision Theory
2 Linear Methods for Classification
3 Sub-space Methods
4 Ensemble Methods
5 Hidden Markov Models
6 Prototype/memory based methods
7 Kernels and other tricks
8 Tree based techniques
9 Large Margin Classifiers
10 Deep Learning
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Discussion Sessions
Discuss two papers per class:
Each paper:
Student presentation of paper ≈ 15 minutes introGroup: What are the main lessons/key insight from the paperGroup: How could it be improved / what would you do differently?TA/Lecturer: guide discussion / presentation
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Outline
1 Introduction
2 Objective / Motivation
3 Schedule / Structure
4 Homework / Exercises
5 Material ...
6 Background examples
7 Questions
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Homework
Leverage of a set of datasets - varying in complexity, ...
A homework assignment roughly every month
4 assignments in total
First three will use the common datasets (Gaussian / Ensemble /Temporal)
Final homework - option to use your own dataset - large margin /deep learning
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Credit / Grading
45% Homeworks 1-3
25% Homework 4
25% Class Presentation / Discussions
5% Class participation
There will no final exam!
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Submission of material
Please submit home work on time.
Late submissions will be 75% for 1 day late, 50% for 2 days late andthen 25% after that
You can ask for permission with a good motivation, but have to do itwell ahead of time (not an hour before!)
Do not expect that we are online the last hour before a deadline.Unfair to the TAs and others.
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Outline
1 Introduction
2 Objective / Motivation
3 Schedule / Structure
4 Homework / Exercises
5 Material ...
6 Background examples
7 Questions
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Book 1: Elements of Statistical Learning
Main textbookElements of Statistical LearningT. Hastie, R. Tibshirani & J.FreiedmanSpringer Verlag, 2nd Edition, 2009http://www-stat.stanford.edu/
~tibs/ElemStatLearn
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Book 2: Machine Learning - Kevin Murphy
Machine LearningK. MurphyMIT-Press, 2013
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Book 3: Duda, Hart and Stork
Pattern ClassificationR. O. Duda, P. E. Hart and D. G.StorkWiley Interscience, 2nd, 2001, ISBN0-471-05669-3
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Software
You can use Matlab or Python - we will try to support both
Some demonstrations using Matlab / K. Murphy Toolkit
https://github.com/probml/pmtk3
Some examples using Scikit-Learn Toolkit
http://scikit-learn.org
Still try to finalize 2-3 datasets for homework
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Outline
1 Introduction
2 Objective / Motivation
3 Schedule / Structure
4 Homework / Exercises
5 Material ...
6 Background examples
7 Questions
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Model based recognition
Google has 2.5 million objects inthe 3D object warehouse
Can we use these for recognitionof objects?
Can we provide context forobject recognition?
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Gesture based recognition
Tracking of hands for person forrobot interaction
Color classification of hands andhead of user
Tracking of objects usingKalman filter
HMM based recognition ofgestures
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Recognition of daily activities
Images of standard objects torecognize daily activities
Example application forassistance to people withmemory challenges
Using Deep Learning forRecognition of situations
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Introduction Objective / Motivation Schedule / Structure Homework / Exercises Material ... Background examples Questions
Outline
1 Introduction
2 Objective / Motivation
3 Schedule / Structure
4 Homework / Exercises
5 Material ...
6 Background examples
7 Questions
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Next Lecture
Thursday - Bayes Decision Theory
DHS: Chapter 2 (2.1-2.6)
We will provide the initial list of papers for class discussions
Discuss the datasets for home work
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Questions?
Questions?
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