Introduction Machine Learning

Август 8, 2022

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Introduction Machine Learning

Содержание

2. Logistics Instructor: Polichshuk Yekaterina Email: polichshuk.y.v@gmail.com Office: 262 TA: Aidos Askhatuly Email: aidos.askhatuly@gmail.com
3. Evaluation
4. Source Materials P. Harrington, Machine learning in Action(Recommended) T. Mitchell, Machine Learning, McGraw-Hill Online courses: udacity.com
5. A Few Quotes “A breakthrough in machine learning would be worth ten Microsofts” (Bill Gates, Chairman,
6. So What Is Machine Learning? Automating automation Getting computers to program themselves Writing software is the
7. Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program
8. Magic? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants =
9. Sample Applications Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging
10. ML in a Nutshell Tens of thousands of machine learning algorithms Hundreds new every year Every
11. Representation Decision trees Sets of rules / Logic programs Instances Graphical models (Bayes/Markov nets) Neural networks
12. Evaluation Accuracy Precision and recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L
13. Optimization Combinatorial optimization E.g.: Greedy search Convex optimization E.g.: Gradient descent Constrained optimization E.g.: Linear programming
14. Types of Learning Supervised (inductive) learning Training data includes desired outputs Unsupervised learning Training data does
15. Inductive Learning Given examples of a function (X, F(X)) Predict function F(X) for new examples X
16. What We’ll Cover Supervised learning Decision tree induction Rule induction Instance-based learning Bayesian learning Neural networks
17. Steps in developing a machine learning application Collect data. Prepare the input data. Analyze the input
18. Programming languages Why Python? Python is a great language for machine learning for a large number
19. Libraries: SciPy
21. Скачать презентацию

Слайд 2

Logistics
Instructor: Polichshuk Yekaterina
Email: polichshuk.y.v@gmail.com
Office: 262
TA: Aidos Askhatuly
Email: aidos.askhatuly@gmail.com

Слайд 3

Evaluation

Слайд 4

Source Materials
P. Harrington, Machine learning in Action(Recommended)
T. Mitchell, Machine Learning, McGraw-Hill

Online courses:
udacity.com - Introduction to machine learning
https://www.udacity.com/course/viewer#!/c-ud120/l-2254358555/e-3012748573/m-3035918544
coursera.org - Machine learning - Andrew Ng
https://www.coursera.org/learn/machine-learning/home/welcome

Слайд 5

A Few Quotes
“A breakthrough in machine learning would be worth ten Microsofts”

(Bill Gates, Chairman, Microsoft)
“Machine learning is the next Internet” (Tony Tether, Director, DARPA)
Machine learning is the hot new thing” (John Hennessy, President, Stanford)
“Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo)
“Machine learning is going to result in a real revolution” (Greg Papadopoulos, CTO, Sun)
“Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo)

Слайд 6

So What Is Machine Learning?
Automating automation
Getting computers to program themselves
Writing software

is the bottleneck
Let the data do the work instead!

Слайд 7

Traditional Programming
Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program

Слайд 8

Magic?
No, more like gardening
Seeds = Algorithms
Nutrients = Data
Gardener = You
Plants

= Programs

Слайд 9

Sample Applications
Web search
Computational biology
Finance
E-commerce
Space exploration
Robotics
Information extraction
Social networks
Debugging
[Your favorite area]

Слайд 10

ML in a Nutshell
Tens of thousands of machine learning algorithms
Hundreds new

every year
Every machine learning algorithm has three components:
Representation
Evaluation
Optimization

Слайд 11

Representation
Decision trees
Sets of rules / Logic programs
Instances
Graphical models (Bayes/Markov nets)
Neural networks
Support

vector machines
Model ensembles
Etc.

Слайд 12

Evaluation
Accuracy
Precision and recall
Squared error
Likelihood
Posterior probability
Cost / Utility
Margin
Entropy
K-L divergence
Etc.

Слайд 13

Optimization
Combinatorial optimization
E.g.: Greedy search
Convex optimization
E.g.: Gradient descent
Constrained optimization
E.g.: Linear programming

Слайд 14

Types of Learning
Supervised (inductive) learning
Training data includes desired outputs
Unsupervised learning
Training data

does not include desired outputs
Semi-supervised learning
Training data includes a few desired outputs
Reinforcement learning
Rewards from sequence of actions

Слайд 15

Inductive Learning
Given examples of a function (X, F(X))
Predict function F(X) for

new examples X
Discrete F(X): Classification
Continuous F(X): Regression
F(X) = Probability(X): Probability estimation

Слайд 16

What We’ll Cover
Supervised learning
Decision tree induction
Rule induction
Instance-based learning
Bayesian learning
Neural networks
Support vector

machines
Model ensembles
Learning theory
Unsupervised learning
Clustering
Dimensionality reduction

Слайд 17

Steps in developing a machine learning application
Collect data.
Prepare the input data.
Analyze

the input data.
Filter garbage
Train the algorithm.
Test the algorithm.
Use it.

Слайд 18

Programming languages
Why Python?
Python is a great language for machine learning for

a large number of reasons.
Python has clear syntax.
it makes text manipulation extremely easy.
A large number of people and organizations use Python, so there’s ample development and documentation.

Слайд 19

Introduction Machine Learning

Содержание

LogisticsInstructor: Polichshuk YekaterinaEmail: polichshuk.y.v@gmail.comOffice: 262TA: Aidos Askhatuly Email: aidos.askhatuly@gmail.com

Evaluation

Source Materials P. Harrington, Machine learning in Action(Recommended)T. Mitchell, Machine Learning, McGraw-Hill

A Few Quotes“A breakthrough in machine learning would be worth ten Microsofts”

So What Is Machine Learning?Automating automationGetting computers to program themselvesWriting software

Traditional ProgrammingMachine LearningComputerDataProgramOutputComputerDataOutputProgram

Magic? No, more like gardeningSeeds = AlgorithmsNutrients = DataGardener = YouPlants

Sample ApplicationsWeb search Computational biologyFinanceE-commerceSpace explorationRoboticsInformation extractionSocial networksDebugging[Your favorite area]

ML in a NutshellTens of thousands of machine learning algorithmsHundreds new

RepresentationDecision treesSets of rules / Logic programsInstancesGraphical models (Bayes/Markov nets)Neural networksSupport

EvaluationAccuracyPrecision and recallSquared errorLikelihoodPosterior probabilityCost / UtilityMarginEntropyK-L divergenceEtc.

OptimizationCombinatorial optimizationE.g.: Greedy searchConvex optimizationE.g.: Gradient descentConstrained optimizationE.g.: Linear programming

Types of LearningSupervised (inductive) learningTraining data includes desired outputsUnsupervised learningTraining data

Inductive LearningGiven examples of a function (X, F(X))Predict function F(X) for

What We’ll CoverSupervised learningDecision tree inductionRule inductionInstance-based learningBayesian learningNeural networksSupport vector

Steps in developing a machine learning applicationCollect data.Prepare the input data.Analyze

Programming languagesWhy Python?Python is a great language for machine learning for

Libraries: SciPy

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