School of EECS Washington State University CptS 570 - Machine - - PowerPoint PPT Presentation

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School of EECS Washington State University CptS 570 - Machine - - PowerPoint PPT Presentation

Jan 11, 2023 416 likes •677 views

CptS 570 Machine Learning School of EECS Washington State University CptS 570 - Machine Learning 1 Course overview What is machine learning? Why do machine learning? Applications Approaches Resources CptS 570 -

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CptS 570 – Machine Learning School of EECS Washington State University

CptS 570 - Machine Learning 1

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 Course overview  What is machine learning?  Why do machine learning?  Applications  Approaches  Resources

CptS 570 - Machine Learning 2

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 Objectives

  • Knowledge of machine learning (ML) foundations,

paradigms and algorithms

  • Techniques for evaluating ML algorithms
  • Practical experience using ML algorithms
  • Current ML issues

 Website

  • www.eecs.wsu.edu/~holder/courses/CptS570.html

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 Assignments

  • Readings
  • Six (6) homeworks (40%)
  • Two (2) exams (20%)
  • Project (20%)
  • Presentation (10%)
  • Critiques and class participation (10%)

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 Textbook

  • Ethem Alpaydin (2010). Introduction to Machine

Learning, Second Edition. MIT Press.

  • www.cmpe.boun.edu.tr/~ethem/i2ml2e

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 What is learning?  What is machine learning?

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 Webster

  • Gain knowledge or understanding of or

skill in by study, instruction or experience

  • Memorize
  • Synonym: Discovery

 Obtain knowledge of for the first time  May imply acquiring knowledge with little effort or conscious intention (as by simply being told) or it may imply study and practice

  • Knowledge

 Knowing something with familiarity gained through experience or association  Facts or ideas acquired by study, investigation, observation,

  • r experience

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Sleep learning?

(www.links999.net)

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 Herbert Simon (1970)

  • Any process by which a system improves its

performance.

 Tom Mitchell (1990)

  • A computer program that improves its performance

at some task through experience.

 Ethem Alpaydin (2010)

  • Programming computers to optimize a performance

criterion using example data or past experience.

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 How is knowledge represented?  How is experience represented?  What is the performance measure?  Knowledge acquisition vs. skill acquisition  Is deduction learning?

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 Automated knowledge engineering

  • Expertise is scarce
  • Codification of expertise is difficult
  • Expertise frequently consists of a set of test cases
  • Data from measurements, but no information or

knowledge

 Only one computer has to learn, then copy  Discover new knowledge  Understand human learning  Systems need to adapt to unknown, dynamic

environments

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 Patient cases  [medical knowledge] 

automated (better?) diagnosis

 Autonomous driving  Speech recognition  Recommendations (Amazon, Netflix)  Prediction (business, financial, environment,

health, energy, …)

 Fraud/intrusion detection

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 Statistics  Pattern recognition  Signal processing  Control  Artificial intelligence  Data mining  Neuroscience  Cognitive science  Psychology

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 Supervised Learning

  • Classification
  • Regression

 Unsupervised Learning

  • Clustering

 Reinforcement Learning

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Income D e b t Default Good Status

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Income D e b t Default Good Status t If Income < t Then Default

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Income D e b t Default Good Status No Loan Loan if Debt < a*Income + b then Loan else No Loan

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Income D e b t Categories 1) Debt exceeds Income 2) High Debt, High Income 3) Low Debt Cluster 3 Cluster 1 Cluster 2

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Debt<50

Income Income

NO YES YES NO NO YES

yes no no <50 50 50- 100 100

>100 >100

<50 50 50- 100 100

>100 >100

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Income D e b t

No Loan Loan

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Income D e b t

No Loan Loan Debt Loan No No Loan 0.123 23 0.203 03 0.117 17 0.545 45 Input Layer Hidden Layer Outpu put Layer Income me

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 Evaluation

  • Which learning approach is better

 Theoretical bounds

  • What is and is not learnable

 Scalability

  • Learning from massive datasets

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 Software

  • Weka (www.cs.waikato.ac.nz/~ml/weka)
  • Machine learning open-source software (mloss.org)

 Data

  • UCI ML Repository (archive.ics.uci.edu/ml)
  • UCI KDD Repository (kdd.ics.uci.edu)
  • Challenges: KDD-Cup, Netflix, …

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 Conferences

  • International Conference on Machine Learning

(ICML)

  • Knowledge Discovery and Data Mining (KDD)
  • IEEE Conference on Data Mining (ICDM)
  • SIAM Data Mining Conference (SDM)
  • Association for the Advancement of AI (AAAI)
  • International Joint Conference on AI (IJCAI)
  • Many more …

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 Journals

  • Machine Learning Journal
  • Journal of Machine Learning Research
  • Data Mining and Knowledge Discovery
  • Many more …

 WWW

  • www.kdnuggets.com (subscribe!)

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 Machine learning is a computational process

that improves performance based on experience.

 Numerous successful methods  Maturing theory  Open and active research area

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