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Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
Lecture2 - Machine Learning
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Lecture2 - Machine Learning

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  • 1. Introduction to Machine Learning Lecture 2 Albert Orriols i Puig aorriols@salle.url.edu i l @ ll ld Artificial Intelligence – Machine Learning Enginyeria i Arquitectura La Salle gy q Universitat Ramon Llull
  • 2. Recap of Lecture 1 Knowledge Kno ledge Search representation We have seen several search techniques: Blind search, heuristic search, adversary search … GAs We have seen several ways of representing our knowledge Logic-based representation, rule-based representation … g p p We have discussed reasoning mechanisms to deal with uncertainty, incompleteness and inconsistency y p y We set the basis. But, the most interesting is still missing Machine learning M hi l i Slide 2 Artificial Intelligence Machine Learning
  • 3. Today’s Agenda What’s Machine Learning Why Machine Learning? Where is ML Headed and Which Are our Goals? Slide 3 Artificial Intelligence Machine Learning
  • 4. What’s Machine Learning Build computer programs that automatically improve p pg yp with experience Can you be more precise? (Mitchell 1997) (Mitchell, Learning = Improving with experience at some task Improve over task T I tk With respect to a performance measure P Based on experience E B d i E.g.: Learn to play checkers T: Play checkers P: % of games won in world tournament E: opportunity to play against self Slide 4 Artificial Intelligence Machine Learning
  • 5. What Does this Involve? Represent the knowledge p g Logic-based representation Rule-based representation Rl b d t ti Frame-based representation … Search toward better solutions Blind search …, but not really efficient! Non-systematic techniques: G GAs, etc. Slide 5 Artificial Intelligence Machine Learning
  • 6. Why Machine Learning? Several factors affected the increasing appeal of ML From the machines point of view: Recent progress in algorithms and theory Computational power is available From the industry point of view: Growing flood of online data GB hours of data: Remote sensors, telescopes scanning the skies, scientific simulations… Budding industry Machine learning may help scientists, businessmen, and engineers Classify and segment data y g Formulate hypotheses Slide 6 Artificial Intelligence Machine Learning
  • 7. Why Machine Learning? There are three special niches for ML: p Data mining: extract information from historical data to help dec s o decision making ag Medical records Extract knowledge to help doctors Software applications that are too complex to build a hard- wired solution for Autonomous driving g Speech recognition Self customizing programs Recommender systems (RS) New generation RS Slide 7 Artificial Intelligence Machine Learning
  • 8. What’s Data Mining in a Picture 1 J. Han, M. Kamber. J Han M Kamber Data Mining Concepts and Mining. Techniques. Morgan Kaufmann, 2006(Second Edition) Slide 8 Artificial Intelligence Machine Learning
  • 9. Do You Have a Definition for DM 1 Many definitions of data mining. A specially interesting y g p y g one is provided by Duda, Hart, & Stork (2002) Data mining is the process of extracting interesting useful and interesting, useful, novel information from data Many other definitions, but for sure, data mining is not Look up an entry in a data base Query a web search engine How this relates to ML? ML provides methods to dig these data Slide 9 Artificial Intelligence Machine Learning
  • 10. Example of DM 1 Ge Given 9714 patient records, each one describing pregnancy and birth Each patient record consists of 215 features Learn to predict Classes of future patients at risk for Emergency Cesarean Section Slide 10 Artificial Intelligence Machine Learning
  • 11. Example of DM 1 O e of t e u es ea ed One o the rules learned: Slide 11 Artificial Intelligence Machine Learning
  • 12. Example 2 of DM 1 Slide 12 Artificial Intelligence Machine Learning
  • 13. Example 3 of DM 1 Slide 13 Artificial Intelligence Machine Learning
  • 14. Example 4 of DM 1 Slide 14 Artificial Intelligence Machine Learning
  • 15. Other Examples of DM 1 Slide 15 Artificial Intelligence Machine Learning
  • 16. 2 Problems Too Difficult to Program by Hand Autonomous Land Vehicle in a Neural Network (ALVINN) ( ) drives 70 mph on highways Perception system which learns to control the NAVLAB vehicles by watching a person drive Slide 16 Artificial Intelligence Machine Learning
  • 17. Self-Customizing Software 3 Originally at www.wisewire.com System that delivered a unique blend of AI with collaborative and content-based filtering Purchased by Lycos, Inc in 1998 Integrated in Lycos products Documents search for and find interested people. No longer available at www.wisewire.com Visit the f ll i Vi it th following webpage for b f more information: http://www.cse.iitb.ac.in/dbms/Data http://www cse iitb ac in/dbms/Data /Papers-Other/Web/wisewire.html Slide 17 Artificial Intelligence Machine Learning
  • 18. Where is All this Headed? Today: y First-generation systems are evolving toward competent systems that ca tackle so e important p ob e s efficiently and scalably a can ac e some po a problems e c e t y a d sca ab y Give me some prove of that Ask Google Yahoo Docomo Labs … Google, Yahoo, Tomorrow T Semantic networks integrated in DM systems Can you image face book mining? DM in many decision processes: marketing, industry, science … DM as individual recommender systems Slide 18 Artificial Intelligence Machine Learning
  • 19. But… Slow Down! Where are we? We are still beginning! What’s thi Wh t’ this course about? b t? Starting in ML, understanding the problems that we can solve now and the f d h future problems bl This course is not a typical ML course in which we will go through different paradigms Engineers solve problems, so this course tries to follow this idea by y describing important challenges presenting one or several of the most influential techniques to address this challenge Slide 19 Artificial Intelligence Machine Learning
  • 20. Next Class Characteristics Desired for ML Methods Summary of the Paradigms that We Won’t Won t Study Summary of the Problems that We Will Study Slide 20 Artificial Intelligence Machine Learning
  • 21. Introduction to Machine Learning Lecture 2 Albert Orriols i Puig aorriols@salle.url.edu i l @ ll ld Artificial Intelligence – Machine Learning Enginyeria i Arquitectura La Salle gy q Universitat Ramon Llull

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