machine learning - wichita state...
TRANSCRIPT
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Machine Learning
CS 697AB Fall 2017
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Administrative Stuff
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Introduction
✤ Instructor: Dr. Kaushik Sinha
✤ 2 lectures per week TR 8:00-9:15 am
✤ Office Hours TR 9:45-10:45 Jabara Hall 243
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Study Groups (2-3 people)
✤ This course will cover non-trivial material, learning in a group makes it less hard and more fun!
✤ It is recommended (but not required)
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Prerequisites
✤ Three pillars of ML:
✤ Statistics / Probability
✤ Linear Algebra
✤ Multivariate Calculus
✤ Should be confident in at least 1/ 3, ideally 2/ 3.
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Grades ...
✤ Your grade is a composite of:
✤ (Homework) (45%)
✤ Exams (Mid-term1, Mid-term 2)(30%)
✤ Final Project (20%)
✤ Class participation (5%)
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Homework
• You can discuss homework with your peers but your submitted answer should be your own!
• Make honest attempt on all questions (45% of your total grade)
• Typically include programming assignment on MATLAB
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Exams
✤ Exams will be (to some degree) based on homework assignments
✤ Best preparation: Make sure you really really understand the homework assignments
✤ 2 Exams: Midterm 1 & 2
✤ Will be 30% of your grade.
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Final Project
✤ 20% of your grade.
✤ Individual projects.
✤ Sufficient details of the project will be provided in class.
✤ You have to “fill in the gaps”
✤ Will require thinking and in-depth study
✤ Details will be posted on course website later
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Cheating
✤ Don’t cheat!
✤ Use your common sense.
✤ I won’t be your friend anymore!
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MACHINE LEARNING!!!
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What is Machine Learning?
✤ Formally: (Mitchell 1997): A computer program A is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
✤ Informally: Algorithms that improve on some task with experience.
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When should we use ML?
✤ Not an ML problem: E.g. traveling salesman, bin packing, 3-sat, etc.
✤ These are well defined problems, that can easily be formalized
✤ What if this is impossible?
✤ E.g. Which picture contains the human, which one contains the dog?
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When should we use ML?
✤ Not ML problems: Traveling Salesman, 3-Sat, etc.
✤ ML Problems: Hard to formalize, but human expert can provide examples / feedback.
✤ Computer needs to learn from feedback.
✤ Is there a sign of cancer in this fMRI scan?
✤ What will the Dow Jones be tomorrow?
✤ Teach a robot to ride a unicycle.
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Sometimes easy for humans, hard for computers
✤ Even 1 year old children can identify gender pretty reliably
✤ Easy to come up with examples.
✤ But impossible to formalize as a CS problem.
✤ You need machine learning!
Male or Female?
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Example:
Clever
Algorithm
Problem: Given an image of a handwritten d igit, what d igit is it?
2
Input:
Output:
Problem:
You have absolutely no
idea how to do this!
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Example: Problem: Given an image of a handwritten d igit, what d igit is it?
Clever
Algorithm
2
Input:
Output:
Problem:
You have absolutely no
idea how to do this!
Good news:
You have examples
0
1
2
3
4
5
6
7
8
9
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Machine Learning
Algorithm
The Machine Learning Approach:
Example: Problem: Given an image of a handwritten d igit, what d igit is it?
0
1
2
3
4
5
6
7
8
9
Clever
Algorithm
2
Input:
Output:
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Machine Learning
Algorithm
Example: Problem: Given an image of a handwritten d igit, what d igit is it?
0
1
2
3
4
5
6
7
8
9
Learned
Algorithm
2
Training Testing
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Handwritten Digits Recognition
✤ (1990-1995) Pretty much solved in the mid nine-tees. (Lecun et al)
✤ Convolutional Neural Networks
✤ Now used by USPS for zip -codes, ATMs for automatic check cashing etc.
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TD-Gammon (1994)
✤ Gerry Tesauro (IBM) teaches a neural network to play Backgammon. The net plays 100K+ games against itself and beats world champion [Neurocomputation 1994]
✤ Algorithm teaches itself how to play so well!!!
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Deep Blue (1997)
✤ IBM’s Deep Blue wins against Kasparov in chess. Crucial winning move is made due to Machine Learning (G. Tesauro).
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Watson (2011)
✤ IBM’s Watson wins the game show jeopardy against former winners Brad Rutters and Ken Jennings.
✤ Extensive Machine Learning techniques were used .
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Face Detection (2001)
✤ Viola Jone’s “solves” face detection
✤ Previously very hard problem in computer vision
✤ Now commodity in off-the-shelf cellphones / cameras
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Grand Challenge (2005)
✤ Darpa Grand Challenge: The vehicle must drive autonomously 150 Miles through the dessert along a d ifficult route.
✤ 2004 Darpa Grand Challenge huge d isappointment, best team makes 11.78 / 150 miles
✤ 2005 Darpa Grand Challenge 2 is completed by several ML powered teams.
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Speech, Netflix, ...
✤ iPhone ships with built-in speech recognition
✤ Google mobile search speech based (very reliable)
✤ Automatic translation
✤ ....
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ML is the engine for many fields...
Machine
Learning
Computer
Vision
Robotics
Computatio
nal
Biology
Natural
Language
Processing
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Internet companies
✤ Collecting massive amounts of data
✤ Hoping that some smart Machine Learning person makes money out of it.
✤ Your future job!
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Example: Webmail
Spam
filtering Given Email,
predict if it is
spam or not.
Ad -
matching Given user
info predict
which ad
will be
clicked on.
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Example: Websearch Ad Matching
Given query, predict which ad will be
clicked on.
Web-search ranking Given query, predict which
document will be clicked
on.
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Example: Google News
Document clustering Given news articles,
automatically identify and
sort them by topic.
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When will it stop?
✤ The human brain is one big learning machine
✤ We know that we can still do a lot better!
✤ However, it is hard . Very few people can design new ML algorithms.
✤ But many people can use them!
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What types of ML are there?
✤ Supervised learning: Given labeled examples, find the right pred iction of an unlabeled example. (e.g. Given annotated images learn to detect faces.)
✤ Unsupervised learning: Given data try to d iscover similar patterns, structure, low d imensional (e.g. automatically cluster news articles by topic)
As far as this course is concerned:
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Basic Setup
Pre-processing
Feature Extraction
Learning
(Post-processing)
Clean up the data.
Boring but necessary.
Use expert knowledge to get
representation of data.
Focus of this course.
Whatever you do when you are done.
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Feature Extraction
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Feature Extraction
Real World
Represent data in terms of vectors.
Features are statistics that describe the data.
Data Vector Space
Each d imension is
one feature.
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✤ Features are statistics that describe the data
✤ Feature: width/height
✤ Pretty good for “1” vs. “2”
✤ Not so good for “2” vs. “3”
16x16
256x1
✤ Feature: raw pixels
✤ Works for d igits (to some degree)
✤ Does not work for trickier stuff
Handwritten digits
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Bag of Words for Images
✤ Image: Interest Points 0
1
0
0
0
3
0
0
0
0
✤ Extract interest points and represent the image as a bag of interest points.
Dictionary of possible interest points.
Sparse Vector
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Text (Bag of Words)
✤ Text documents: Bag of Words 0
1
0
0
0
2
0
0
0
0
✤ Take d ictionary with n words. Represent a text document as n d imensional vector, where the i-th d imension contains the number of times word i appears in the document.
in
into
...
is
...
...
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Audio? Movies?
✤ Use a slid ing window and Fast Fourier Transform
QuickTime™ and aPhoto - JPEG decompressor
are needed to see this picture.
✤ Treat it as a sequence of images
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Feature Space
✤ Everything that can be stored on a computer can stored as a vector
✤ Representation is critical for successful learning. [Not in this course, though.]
✤ Throughout this course we will assume data is just points in a Feature Space
✤ Important d istinction: sparse / dense
Every
feature is
present
Most
features
are zero
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Mini-Quiz
✤ T/F: Every trad itional CS problem is also an ML problem. FALSE
✤ T/F: Image Features are always dense. FALSE
✤ T/F: The feature space can be very high d imensional. TRUE
✤ T/F: Bag of words features are sparse. TRUE
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Mini-Quiz
✤ T/F: Every trad itional CS problem is also an ML problem. FALSE
✤ T/F: Image Features are always dense. FALSE
✤ T/F: The feature space can be very high d imensional. TRUE
✤ T/F: Bag of words features are sparse. TRUE
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