The CSE 471 Commercial Listening nonstop for 150

The CSE 471 Commercial • Listening non-stop for 150 min per week, for 16 weeks – 4000$ (your tuition). . • Catching up on your beauty sleep in the class – 300$ (chairs not very comfy) • Redoing the in class exam at home – 300$ (lost family time) • Keeping up with the 17, 578, 940 billion bytes of email/blog – 20$ (skimming cost) + 1000$ (Brain frying cost) • Spending most of your life these last four months hacking lisp or doing home works or writing blog comments for CSE 471 – Priceless… ? ? What is Stockholm Syndrome and why is it relevant to CSE 471?

Announcements • Final homework due today; solutions will be posted by tomorrow • Participation sheet to be filled and returned today • CEAS Evaluations online. https: //intraweb. eas. asu. edu/eval – Do take part! • Final exam choice – In Class (on Tuesday 5/12 9: 50— 11: 40 AM) – Take-home (will be given out before the weekend, and will be due by Tuesday 5/12) – Don’t take. .

Take-home vs. In-class • Get’s done fast—at most 2 hours of pain • Get’s graded fast— easy to grade blank sheets • Take-home • Doesn’t get done fast (You may spend a lot of time doing it) • Doesn’t get graded fast —people tend to put up a fight and fill pages

What we did • • Week 6: KR & Prop logic Week 7: prop logic Week 1: Intro; Intelligent agent design [R&N Ch 1, Ch 2] Week 2: Problem Solving Agents [R&N Ch 3 3. 1 --3. 5] Week 3: Informed search [R&N Ch 3 3. 1 --3. 5] Week 4: CSPs and Local Search[R&N Ch 5. 1 --5. 3; Ch 4 4. 3] Week 5: Local Search and Propositional Logic[R&N Ch 4 4. 3; Ch 7. 1 --7. 6] Week 6: Propositional Logic --> Plausible reasoning[R&N Ch 7. 1 --7. 6; [ch 13 13. 1 --13. 5]] • • Week 7: Representations for Reasoning with Uncertainty[ch 13 13. 1 --13. 5]] Week 8: Bayes Nets: Specification & Inference[ch 13 13. 1 --13. 5]] Week 9: Bayes Nets: Inference[ch 13 13. 1 --13. 5]] (Here is a fully worked out example of variable elimination) Week 10: Sampling methods for Bayes net Inference; First-order logic start[ch 13. 5; ] Week 11: Unification, Generalized Modus-Ponens, skolemization and resolution refutation. Week 12: Reasoning with change Planning Week 13: Planning, MDPs & Gametree search Week 14: Learning

Representation Mechanisms: Logic (propositional; first order) Probabilistic logic Learning the models How the course topics stack up… Search Blind, Informed Planning Inference Logical resolution Bayesian inference

Learning Dimensions: What can be learned? --Any of the boxes representing the agent’s knowledge --action description, effect probabilities, causal relations in the world (and the probabilities of causation), utility models (sort of through credit assignment), sensor data interpretation models What feedback is available? --Supervised, unsupervised, “reinforcement” learning --Credit assignment problem What prior knowledge is available? -- “Tabularasa” (agent’s head is a blank slate) or pre-existing knowledge

Chapters Covered • • Table of Contents (Full Version) Preface (html); chapter map Part I Artificial Intelligence 1 Introduction 2 Intelligent Agents Part II Problem Solving 3 Solving Problems by Searching 4 Informed Search and Exploration 5 Constraint Satisfaction Problems 6 Adversarial Search Part III Knowledge and Reasoning 7 Logical Agents 8 First-Order Logic 9 Inference in First-Order Logic 10 Knowledge Representation Part IV Planning 11 Planning (pdf) 12 Planning and Acting in the Real World • Part V Uncertain Knowledge and Reasoning 13 Uncertainty 14 Probabilistic Reasoning 15 Probabilistic Reasoning Over Time 16 Making Simple Decisions 17 Making Complex Decisions Part VI Learning 18 Learning from Observations 19 Knowledge in Learning 20 Statistical Learning Methods 21 Reinforcement Learning Part VII Communicating, Perceiving, and Acting 22 Communication 23 Probabilistic Language Processing 24 Perception 25 Robotics Part VIII Conclusions 26 Philosophical Foundations 27 AI: Present and Future

It matters not what you cover, but what you uncover A Farside treasury…

Schindler: I could've got more. . . I could've got more, if I'd just. . . I could've got more. . . Stern: Oskar, there are eleven hundred people who are alive because of you. Look at them. Schindler: If I'd made more money. . . I threw away so much money, you have no idea. If I'd just. . . Stern: There will be generations because of what you did. Schindler: I didn't do enough. Stern: You did so much. Schindler: This car. Goeth would've bought this car. Why did I keep the car? Ten people, right there. Ten people, ten more people. . . (He rips the swastika pin from his lapel) This pin, two people. This is gold. Two more people. He would've given me two for it. At least one. He would've given me one. One more person. A person, Stern. For this. I could've gotten one more person and I didn't. Top few things I would have done if I had more time • Statistical Learning • Reinforcement Learning; Bagging/Boosting • Planning under uncertainty and incompleteness • Ideas of induced tree-width • Multi-agent X (X=search, learning. . ) • PERCEPTION (Speech; Language…) • Be less demanding more often (or even once…) Adieu with an Oscar Schindler Routine. Rao: I could've taught more. . . I could've taught more, if I'd just. . . I could've taught more. . . Yunsong: Rao, there are thirty people who are mad at you because you taught too much. Look at them. Rao: If I'd made more time. . . I wasted so much time, you have no idea. If I'd just. . . Yunsong: There will be generations (of bitter people) because of what you did. Rao: I didn't do enough. Yunsong: You did so much. Rao: This slide. We could’ve removed this slide. Why did I keep the slide? Two minutes, right there. Two minutes, two more minutes. . This music, a bit on reinforcement learning. This review. Two points on bagging and boosting. I could easily have made two for it. At least one. I could’ve gotten one more point across. One more point. A point, Yunsong. For this. I could've gotten one more point across and I didn't.

--Marvin Minsky Here is hoping you too experienced a sense of loss this semester…