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CSE 312 Foundations of Computing II

CSE 312 Foundations of Computing II. Instructor: Pedro Domingos. Logistics. Instructor: Pedro Domingos Email: pedrod@cs Office hours: Fridays 2:30-3:20, CSE 648 TA 1: Aniruddh Nath Email: nath@cs Office hours: Wednesdays 2:30-3:20, CSE 218 TA 2: Boris Kogon Email: boris@cs

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CSE 312 Foundations of Computing II

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  1. CSE 312Foundations of Computing II Instructor: Pedro Domingos

  2. Logistics • Instructor: Pedro Domingos • Email: pedrod@cs • Office hours: Fridays 2:30-3:20, CSE 648 • TA 1: Aniruddh Nath • Email: nath@cs • Office hours: Wednesdays 2:30-3:20, CSE 218 • TA 2: Boris Kogon • Email: boris@cs • Office hours: Mondays 3:30-4:20, CSE 216 • Web:www.cs.washington.edu/312 • Mailing list: cse312a_sp11@uw.edu

  3. Evaluation • Four homeworks (16% each) • Handed out on Friday on weeks 1, 3, 5 and 7 • Due two before class two weeks later • Final (36%)

  4. Textbooks • D. Bertsekas & J. Tsitsiklis, Introduction to Probability (2nd ed.), Athena (Required) • S. Dasgupta, C. Papadimitriou &U. Vazirani, Algorithms, McGraw-Hill (Required; free online) • K. Rosen, Discrete Mathematics and its Applications, (6th. Ed.), McGraw-Hill (Recommended)

  5. What Is this Course About? • First 20 lectures:Probability and statistics • Last 10 lectures:Algorithms and NP-completeness

  6. Web search Web advertising Spam filtering Collaborative filtering Personalization Machine learning Information integration Sensor networks Performance analysis Algorithm design Scientific data analysis Life in general Why Is Probability Important? “Old” CS: Deterministic “New” CS: Probabilistic

  7. Probability • Counting • Basics of probability • Conditional probability • Random variables • Discrete and continuous distributions • Expectation and variance • Tail bounds and central limit theorem

  8. Statistics • Maximum likelihood estimation • Bayesian estimation • Hypothesis testing • Linear regression • Machine learning

  9. Algorithms • Polynomial-time algorithms • Divide and conquer • Dynamic programming • NP-completeness • Satisfiability • Reductions

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