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Lecture 0: Machine Learning
Tuo Zhao
Schools of ISYE and CSE, Georgia Tech
2017 Fall
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Questions
Course Logistics
Why Machine Learning?
What is a well-defined learning problem?
What questions should we ask about Machine Learning?
Tuo Zhao — Lecture 0: Machine Learning 2/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Machine Learning is Interdisciplinary
Tuo Zhao — Lecture 0: Machine Learning 3/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Pre-requisites
Math:
Calculus and Linear Algebra
Probability and Statistics
Basic Optimization
Coding:
MATLAB for coding HW (No Exception)
Plus:
Generalized Linear Models
Convex Optimization
Tuo Zhao — Lecture 0: Machine Learning 4/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Course Logistics
Teaching Assistants:
Shaojun Ma: Ph.D. Student in CEE
Yujia Xie: Ph.D. Student in CSE
Minshuo Chen: Ph.D. Student in ISYE
Haoming Jiang: Ph.D. Student in ISYE
Zhehui Chen: Ph.D. Student in ISYE
TBD
See http://www2.isye.gatech.edu/~tzhao80/others.html
Syllabus, Lecture Slides
Homework Assignments
Tuo Zhao — Lecture 0: Machine Learning 5/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Highlights of Course Logistics
Working Load:
Background Knowledge Test: 6%
4 Written HW: 24%
3 Coding HW: 18%
Exam-1: 26%
Exam-2: 26%
See https://piazza.com/class/j4ujbo0admd2in
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Tuo Zhao — Lecture 0: Machine Learning 6/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Distance Learning
Working Load:
5 Written HW: 60%
4 Coding HW: 40%
No Background Knowledge Test
No Mid-term Exam
Late Homework Policy for All Students:
No Late Homework Accepted!
Always due at noon on Friday
Tuo Zhao — Lecture 0: Machine Learning 7/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Knowledge Background Test Statistics
Top 10%: 30/40
Top 25%: 27/40
Top 50%: 20/40
Top 75%: 14/40
Maximum: 38
Suggestions – Go through the review materials carefully!
Tuo Zhao — Lecture 0: Machine Learning 8/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Remarks
Office hours for asking questions
Sep. 19 – A more difficult make-up exam (but will be curvedaccordingly)
No time for answering questions after class
You need to debug by yourself
Honor Code
Tuo Zhao — Lecture 0: Machine Learning 9/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
What to Cover?
Methodology and Algorithms of Machine Learning.
Some Theory for Ph.D. Students.
Some homework problems will be for Ph.D. students ONLY.
Different letter grades for each section.
Not About “Introduction to Machine Learning”
Not About “How to Apply Machine Learning to YourDomain”.
Not About “How to Use Software to Do Machine Learning”
Tuo Zhao — Lecture 0: Machine Learning 10/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Alternative
Easier: CS 4641 Machine Learning
Signal Processing: ECE 6254: Statistical Machine Learning
Learning Theory: CS 7545 Machine Learning Theory
More Foundation: CS 8803 Mathematical Foundations ofMachine Learning
Applications to Specific Domains: Computer Vision, NaturalLanguage Processing, etc.
Tuo Zhao — Lecture 0: Machine Learning 11/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Why Machine Learning?
Recent progress in algorithms and theories
Growing flood of massive data
Computational power is available
Budding industry
Tuo Zhao — Lecture 0: Machine Learning 12/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Why Machine Learning?
Three Niches for Machine Learning:
Data mining: using historical data to improve decisions
medical records → medical knowledge
Software applications we can’t program by hand
autonomous drivingspeech recognition
Self customizing programs
Newsreader that learns user interests
Tuo Zhao — Lecture 0: Machine Learning 13/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
What is the Learning Problem?
Learning: Improving with experience at some task
Improve over task T
with respect to performance measure P
based on experience E
Example: Learn to play checkers
T : Play checkers
P : % of games won in world tournament
E: opportunity to play against self
Tuo Zhao — Lecture 0: Machine Learning 14/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Learn to Predict Emergent C-sections
Data:
2
Learning to Predict Emergency C-Sections
9714 patient records, each with 215 features
[Sims et al., 2000]
Learning to detect objects in images
Example training images for each orientation
(Prof. H. Schneiderman)
One of 18 learned rules:
2
Learning to Predict Emergency C-Sections
9714 patient records, each with 215 features
[Sims et al., 2000]
Learning to detect objects in images
Example training images for each orientation
(Prof. H. Schneiderman)
Tuo Zhao — Lecture 0: Machine Learning 15/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Learn to Detect Objects in Images
Tuo Zhao — Lecture 0: Machine Learning 16/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Learn to Classify Documents
Tuo Zhao — Lecture 0: Machine Learning 17/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Learn to Drive Autonomously
Tuo Zhao — Lecture 0: Machine Learning 18/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Learn to Recognize Speech
Tuo Zhao — Lecture 0: Machine Learning 19/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Learn to Translate Languages
Tuo Zhao — Lecture 0: Machine Learning 20/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Learn to Play Computer Games
Tuo Zhao — Lecture 0: Machine Learning 21/22
ISYE6740/CSE6740/CS7641: Computational Data Analysis/Machine Learning
Next 3 Lectures
Linear Algebra Review (2 Lectures)
Probability Review (1 Lecture)
Tuo Zhao — Lecture 0: Machine Learning 22/22