K-State CIS 830 - Analytical Learning and Data Engineering

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1Kansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceWednesday, January 19, 2000William H. HsuDepartment of Computing and Information Sciences, KSUhttp://www.cis.ksu.edu/~bhsuReadings:Chapter 21, Russell and NorvigFlann and DietterichAnalytical Learning and Data Engineering:OverviewLecture 1Lecture 1Kansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceLecture OutlineLecture Outline• Quick Review– Output of learning algorithms• What does it mean to learn a function?• What does it mean to acquire a model through (inductive) learning?– Learning methodologies• Supervised (inductive) learning• Unsupervised, reinforcement learning• Inductive Learning– What does an inductive learning problem specification look like?– What does the “type signature” of an inductive learning algorithm mean?– How do inductive learning and inductive bias work?• Analytical Learning– How does analytical learning work and what does it produce?– What are some relationships between analytical and inductive learning?• Integrating Inductive and Analytical Learning for KDDKansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceIntroductionsIntroductions• Student Information (Confidential)– Instructional demographics: background, department, academic interests– Requests for special topics• Lecture• Project• On Information Form, Please Write– Your name– What you wish to learn from this course– What experience (if any) you have with• Artificial intelligence• Probability and statistics– What experience (if any) you have in using KDD (learning, inference; ANN, GA,probabilistic modeling) packages– What programming languages you know well– Any specific applications or topics you would like to see coveredKansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceIn-Class ExerciseIn-Class Exercise• Turn to A Partner– 2-minute exercise– Briefly introduce yourselves (2 minutes)– 3-minute discussion– 10-minute go-round– 3-minute follow-up• Questions– 2 applications of KDD systems to problem in your area–Common advantage and obstacle• Project LEA/RN™ Exercise, Iowa State [Johnson and Johnson, 1998]– Formulate an answer individually– Share your answer with your partner– Listen carefully to your partner’s answer– Create a new answer through discussion– Account for your discussion by being prepared to be called uponKansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceAbout Paper ReviewsAbout Paper Reviews• 20 Papers– Must write at least 15 reviews– Drop lowest 5• Objectives– To help prepare for presentations and discussions (questions and opinions)– To introduce students to current research topics, problems, solutions,applications• Guidelines– Original work, 1-2 pages•Do not just summarize• Cite external sources properly– Critique• Intended audience?• Key points: significance to a particular problem?• Flaws or ways you think the paper could be improved?Kansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceAbout PresentationsAbout Presentations• 20 Presentations– Every registered student must give at least 1– If more than 20 registered, will assign duplicates (still should be original work)– First-come, first-served (sooner is better)• Papers for Presentations– Will be available at 14 Seaton Hall by Monday (first paper: online)– May present research project in addition / instead (contact instructor)• Guidelines– Original work, ~30 minutes•Do not just summarize• Cite external sources properly– Presentations• Critique• Don’t just read a paper review: help the audience understand significance• Be prepared for 20+ minutes of questions, discussion2Kansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial Intelligence• Classification Functions– Learning hidden functions: estimating (“fitting”) parameters– Concept learning (e.g., chair, face, game)– Diagnosis, prognosis: medical, risk assessment, fraud, mechanical systems• Models– Map (for navigation)– Distribution (query answering, aka QA)– Language model (e.g., automaton/grammar)• Skills– Playing games– Planning– Reasoning (acquiring representation to use in reasoning)• Cluster Definitions for Pattern Recognition– Shapes of objects– Functional or taxonomic definition•Many Problems Can Be Reduced to ClassificationQuick Review:Quick Review:Output of Learning AlgorithmsOutput of Learning AlgorithmsKansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceQuick Review:Quick Review:Learning MethodologiesLearning Methodologies• Supervised– What is learned? Classification function; other models– Inputs and outputs? Learning:– How is it learned? Presentation of examples to learner (by teacher)• Unsupervised– Cluster definition, or vector quantization function (codebook)– Learning:– Formation, segmentation, labeling of clusters based on observations, metric• Reinforcement– Control policy (function from states of the world to actions)– Learning:– (Delayed) feedback of reward values to agent based on actions selected; modelupdated based on reward, (partially) observable state() ()xfxfx,ˆionapproximatexamples →() ()xfx,xdx21codebook discretemetric distancensobservatio →×{}as:pnir,si→→≤≤ policy1 sequence rdstate/rewai:Kansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceUnknownFunctionx1x2x3x4y = f (x1, x2, x3, x4 )•xi: ti, y: t, f: (t1 × t2 × t3 × t4) → t• Our learning function: Vector (t1 × t2 × t3 × t4 × t) → (t1 × t2 × t3 × t4) → t Example x1 x2 x3 x4 y 0 0 1 1 0 0 1 0 0 0 0 0 2 0 0 1 1 1 3 1 0 0 1 1 4 0 1 1 0 0 5 1 1 0 0 0 6 0 1 0 1 0 Example:Example:Inductive Learning ProblemInductive Learning ProblemKansas State UniversityDepartment of Computing and Information SciencesCIS 830: Advanced Topics in Artificial IntelligenceQuick Review:Quick


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