Programming Paradigms • Course overview •Introduction to programming paradigms Copyright By PowCoder代写 加微信 powcoder • Review: The object-oriented paradigm in Java •Imperative and concurrent programming paradigm: Go. • Logic paradigm: Prolog. • Functional paradigm: Scheme. Announcement • Quiz 3 is posted due Thursday Feb 14th • Extra quiz in Go is posted (Best 9 will […]
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Uninformed Search AIMA 3.3-3.4; Appendix A.1 CMPSC 442 Week 2, Meeting 7, Segment 4 of 4: Comparison of Search Algorithms UCS Evaluation ● Complete, like BFS ● Optimal for any step cost function ● Time complexity contingent on step costs ○ Let ε = min a∈A cost(a) and consider the case ε > 0 (lower
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Reinforcement Learning Introduction AIMA 22.1-22.2 CMPSC 442 Week 12, Meeting 36, Three Segments Outline ● Overview of Reinforcement Learning ● Direct Estimation vs. Adaptive Dynamic Programming ● Temporal Difference Learning 2Outline, Wk 12, Mtg 36 Reinforcement Learning Introduction AIMA 22.1-22.2 CMPSC 442 Week 12, Meeting 36, Segment 1 of 3: Overview of Reinforcement Learning (RL)
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Name: CIS 521 Page 2 of 7 Question 1. [20] True-False & Multiple Choice [All questions worth 2 points.] a) In Python, what does the last command in this sequence print? > x = [[(x, y) for x in 1,2,3] for y in 1,2,3] > len(x) (a) 1 (b) 3 (c) 6 (d) 9 b)
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On Beyond Minimax and Games AIMA 5.3 – 5.5 CMPSC 442 Week 5, Meeting 13, Three Segments Outline ● Heuristic Alpha-Beta, and Other Hybrid Search ● Monte Carlo Tree Search ● Stochastic Games: Expectiminimax ● Other Variants, and Limitations Outline, Wk 5, Mtg 13 2 On Beyond Minimax and Games AIMA 5.3 – 5.5 CMPSC
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CMPSC442-Wk12-Mtg35 Introduction to Deep Learning and Neural Networks AIMA 21.1 – 21.6 CMPSC 442 Week 12, Meeting 35, Three Segments Outline ● Intro to Deep Learning and Neural Networks ● Computation Graphs ● Convolutional Networks versus Recurrent Networks 2Outline, Wk 12, Mtg 35 CMPSC 442 Week 12, Meeting 35, Segment 1 of 3: Intro to
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Learning HMMs: Baum-Welch J&M Appendix A CMPSC 442 Week 10, Meeting 28, Three Segments Outline ● Backward algorithm ● Formulae for estimating the parameters ● Baum-Welch 2Outline, Wk 10, Mtg 28 Learning HMMs: Baum-Welch J&M Appendix A CMPSC 442 Week 10, Meeting 28, Segment 1 of 3: Backward Algorithm Developing the Parameters for an HMM
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NAME: CMPSC 442 SID: FINAL EXAM: 100 points FALL 2017 INSTRUCTIONS: Write your name and SID at the top of every page. Write your answers on the exam sheet in the white space below the questions. When you have finished, show your id when you turn in your exam. Question Maximum Points Actual Points 1a 2.5 1b 2.5 1c 2.5 1d 2.5 1e 10.0 2a 2.5 2b 2.5 2c 5.0 2d 10.0 3a 2.5 3b 2.5 3c 10.0 4 25.0 5a 2.5 5b 2.5 5c
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CMPSC442-Wk8-Mtg24 Bayesian Inference AIMA 13.3-13.5 CMPSC 442 Week 8, Meeting 24, Three Segments Outline ● Exact Inference in Bayesian Networks ● Approximate Inference for Bayesian Networks ● Causal Networks 2Outline, Wk 8, Mtg 24 Bayesian Inference AIMA 13.3-13.5 CMPSC 442 Week 8, Meeting 24, Segment 1 of 3: Exact Inference in Bayesian Networks General Motivation
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FOL Inference AIMA 9 CMPSC 442 Week 6, Meeting 18, Four Segments Outline ● Propositional and First-Order Inference ● Unification and First-Order Inference ● Forward Chaining vs. Backward Chaining ● Resolution in FOL 2Outline, Wk 6, Mtg 18 FOL Inference AIMA 9 CMPSC 442 Week 6, Meeting 17, Segment 1 of 4: Propositional and First-Order
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