COMP90038 Algorithms and Complexity Lecture 4: Analysis of Algorithms (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray Last Time: Time Complexity Measure input size by natural number n Measure execution time as number of basic How to compare different t(n) ? Asymptotic growth rate O(g(n)), Ω(g(n)), Θ(g(n)) Copyright University […]
THE AUSTRALIAN NATIONAL UNIVERSITY Second Semester 2019 COMP1600/COMP6260 (Foundations of Computation) Writing Period: 3 hours duration Study Period: 15 minutes duration Permitted Materials: One A4 page with hand-written notes on both sides Answer ALL questions Total marks: 100 The questions are followed by labelled blank spaces into which your answers are to be written. Additional
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COMP90038 Algorithms and Complexity Lecture 10: Decrease-and-Conquer-by-a-Factor (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray 2 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Decrease-and-Conquer Last lecture: to solve a problem of size n, try to express the solution in terms of a solution to the
Neural Net Introduction Reinforcement Learning Main sources: Marsland “Machine Learning” (CRC) Chapter 11 Sutton and Barto “Reinforcement Learning” (MIT) ‹#› © 2012-15 In reinforcement learning, a random action is taken. If it leads to a favorable outcome, it is strengthened. Learning Goals Recognize Reinforcement Learning potential Use basic RL techniques Apply Monte Carlo decision processes
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Nondeterministic Finite Automata COMP1600 / COMP6260 Australian National University Semester 2, 2021 DFA Minimisation Elimination of equivalent states. if two states are equivalent, one can be eliminated Elimination of Unreachable States if a state cannot be reached from the initial state then it can also be eliminated. Example. S not reachable ?0 3
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STAT318 — Data Mining Dr University of Canterbury, Christchurch, , University of Canterbury 2021 STAT318 — Data Mining ,1 / 28 Association Analysis Association analysis is an unsupervised learning technique that finds interesting associations in large data sets. It is often applied to large commercial data bases, where it is useful for selective marketing. If
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STAT318 — Data Mining Dr University of Canterbury, Christchurch, Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. , University of Canterbury 2020 STAT318 — Data Mining ,1 /
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STAT318/462 — Data Mining Dr G ́abor Erd ́elyi University of Canterbury, Christchurch, Course developed by Dr B. Robertson. Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. G.
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COMP90038 Algorithms and Complexity Lecture 6: Recursion (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray 2 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Recursion We’ve already seen some examples • A very natural approach when the data structure is • recursive (e.g. lists, trees) But
COMP90038 Algorithms and Complexity Lecture 12: More Divide-and-Conquer Algorithms (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray 2 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Divide and Conquer In the last lecture we studied the archetypal divide- and-conquer sorting algorithms: mergesort and quicksort. We also