Faculty of Natural Sciences Eric Chen Goal Tutorial 2 is a preparation for the lab 1. The goal of this tutorial is to provide practices on algorithm development and program development process in C. Please read the lab1 instruction before you come to Tutorial 2. Formatted Output In Task 1 and Task 2 of Lab […]
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COMP3308/3608, Lecture 6a ARTIFICIAL INTELLIGENCE Statistical-Based Learning (Naïve Bayes) Reference: Witten, Frank, Hall and Pal, ch.4.2: p.96-105 Russell and Norvig, ch.20: p.802-810 Irena Koprinska, irena.koprinska@sydney.edu.au COMP3308/3608 AI, week 6a, 2021 1 • Bayes theorem • Naïve Bayes algorithm • Naïve Bayes – issues • Zero probabilities – Laplace correction • Dealing with missing values •
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COMP3308/3608 Introduction to Artificial Intelligenece (regular and advanced) semester 1, 2020 Information about the exam The exam will be online, via Canvas, un-proctored. It is set as a Quiz. The Canvas site for the exam is different that the Canvas site we use during the semester. There are 2 exam sites: one for
COMP3308/3608 Artificial Intelligence Week 2 Tutorial exercises Search Problem Formulation. Uninformed Search. Informed Search: Greedy. Welcome to your first AI tutorial! Please note the following: • The tutorial notes typically include more exercises than what we can do in the 1-hour tutorial. We will do only some of them in class, but you need to
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COMP3308/3608 Artificial Intelligence Week 3 Tutorial exercises A* algorithm. Heuristics. Local search algorithms. Exercise 1 (Homework). A* search Consider the tree below. Step costs are shown along the edges and heuristic values h are shown in brackets. The goal nodes are circled twice, i.e. they are C, I, E and K. S 43 COMP3308/3608 Artificial
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COMP3308/3608, Lecture 8b & 9a ARTIFICIAL INTELLIGENCE Multilayer Neural Networks and Backpropagation Algorithm Reference: Russell and Norvig, pp. 731-737 Witten, Frank and Hall, pp. 232-241 Irena Koprinska, irena.koprinska@sydney.edu.au COMP3308/3608 AI, week 8b & 9a, 2021 1 • Multilayer perceptron NNs • XOR problem • neuron model • Backpropagation algorithm • Derivation • Example • Universality
COMP3308/3608 Artificial Intelligence Week 4 Tutorial exercises Game Playing Exercise 1. Adversarial search Why does search in game playing programs always proceed forward from the current position rather than backward from the goal? Exercise 2 (Homework). Minimax and alpha-beta Consider the following game in which the evaluation function values for the MAX player are shown
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COMP3308/3608 Artificial Intelligence Weeks 5 Tutorial exercises Introduction to Machine Learning. K-Nearest Neighbor and 1R. Exercise 1 (Homework). K-Nearest Neighbor with numeric attributes Consider the dataset given below where years_experience is the only attribute and salary is the class. What will be the prediction for the new example years_experience=5 using 1-Nearest-Neighbor and 3- Nearest-Neighbor with
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COMP3308/3608, Lecture 10b ARTIFICIAL INTELLIGENCE Ensembles of Classifiers Reference: Russell and Norvig, pp. 748-753 Witten, Frank, Hall and Pal, ch.12 Irena Koprinska, irena.koprinska@sydney.edu.au COMP3308/3608 AI, week 10b, 2021 1 Outline • Ensembles of learning algorithms • Motivation • Creating ensembles by: • Manipulating the training data • Bagging • Boosting • Manipulating the attributes •
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COMP3308/3608 Artificial Intelligence Week 8 Tutorial exercises Perceptrons. Multilayer Neural Networks 1. Exercise 1. Perceptron learning (Homework) The homework for this week consists of 5 multiple-choice questions with two possible answers – True and False. To complete the homework, please go to Canvas -> Quizzes->w8-homework-submission. Remember to press the “Submit” button at the end. Only
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