### COMP3308/3608

matlab | Network | network | 代做Algorithm | Artificial | 代写quiz | lab – 该题目是一个常规的network的练习题目代写, 涉及了matlab/Network/network/Algorithm/Artificial等代写方面, 这个项目是lab代写的代写题目

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### COMP3308/3608 Introduction to Artificial Intelligenece (regular and

### advanced)

### semester 1, 2020

### Information about the exam

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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 called Final Exam for
COMP3308 and another one for COMP3608 - Final Exam for COMP3608. The
Exams Office will give you access to your exam site.
Students who are not in Australia and are in different time zones may apply for special
arrangements for the exam:
https://www.sydney.edu.au/students/special-consideration.html#time-zone
The duration of the exam is standard: 2 hours + 10 minutes reading time = 130 min.
The Canvas quiz will open at the scheduled time (as per your exam timetable) and close
after 130 mins.
The exam is worth 60 marks ( = 60 % of your final mark). To pass the course you need
at least 40% on the exam (i.e. 24 marks), regardless of what your mark during the
semester is.
All material is examinable except week 1 , week 13a (recommender systems), historical
context, mat lab and Weka.
Tip: Have a calculator ready as there are some calculation questions.
There are 3 types of questions: 1) problem solving, 2) questions requiring short answers,
and 3) multiple-choice questions (a small number).
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### Sample exam questions

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In addition to these questions please also see on Canvas:
Search: Quiz_Practice _with_Quokkas.pdf
Bayesian networks: BN_practice_questions.pdf
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Question 1. In the tree below the step costs are shown along the edges and the h values are
shown next to each node. The goal nodes are double-circled: F and D.
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Write down the order in which nodes are expanded using:
a) Breadth-first search
b) Depth-first search
c) Uniform cost search
d) Iterative deepening search
e) Greedy search
f) A*
In case of ties, expand the nodes in alphabetical order.
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Question 2. Answer briefly and concisely:
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a) A* uses admissible heuristics. What happens if we use a non-admissible one? Is it still
useful to use A* with a non-admissible heuristic?
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b) What is the advantage of choosing a dominant heuristic in A* search?
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c) What is the main advantage of hill climbing search over A* search?
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Question 3. Consider the following game in which the evaluation function values for player
MAX are shown at the leaf nodes. MAX is the maximizing player and MIN is the minimizing
player. The first player is MAX.
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a) What will be the backed-up value of the root node computed by the minimax
algorithm?
b) Which move should MAX choose based on the minimax Algorithm to node B, C or
D?
c) Assume that we now use the alpha-beta algorithm. List all branches that will be pruned,
e.g. AB etc. Assume that the children are visited left-to-right (as usual).
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### Question 4. Answer briefly and concisely:

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a) The 1R algorithm generates a set of rules. What do these rules test?
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b) Gain ratio is a modification of Gain used in decision trees. What is its advantage?
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c) Propose two strategies for dealing with missing attribute values in learning algorithms.
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d) Why do we need to normalize the attribute values in the k-nearest-neighbor algorithm?
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e) What is the main limitation of the perceptrons?
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f) Describe an early stopping method used in the backpropagation algorithm to prevent
overfitting.
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g) The problem of finding a decision boundary in support vector machine can be
formulated as an optimisation problem using Lagrange multipliers. What are we
maximizing?
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h) In linear support vector machines, we use dot products both during training and
during classification of a new example. What vectors are these products of?
During training:
During classification of a new example:
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Question 5. Consider the task of learning to classify mushrooms as safe or poisonous based
on the following four features: stem = {short, long}, bell = {rounded, flat}, texture = {plain,
spots, bumpy, ruffles} and number = {single, multiple}.
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The training data consists of the following 10 examples:
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Safe:
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Poisonous:
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a) Use Nave Bayes to predict the class of the following new example:
Show your calculations.
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b) How would 3-Nearest Neighbor using the Hamming distance classify the same example
as above?
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c) Consider building a decision tree. Calculate the information gain for texture and number.
Which one of these two features will be selected?
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You may use this table:
x y - (x/y)*log 2 (x/y) x y - (x/y)*log 2 (x/y x y - (x/y)*log 2 (x/y x y - (x/y)*log 2 (x/y
1 2 0.50 4 5 0.26 6 7 0.19 5 9 0.
1 3 0.53 1 6 0.43 1 8 0.38 7 9 0.
2 3 0.39 5 6 0.22 3 8 0.53 8 9 0.
1 4 0.5 1 7 0.40 5 8 0.42 1 10 0.
3 4 0.31 2 7 0.52 7 8 0.17 3 10 0.
1 5 0.46 3 7 0.52 1 9 0.35 7 10 0.
2 5 0.53 4 7 0.46 2 9 0.48 9 10 0.
3 5 0.44 5 7 0.35 4 9 0.
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d) Consider a single perceptron for this task. What is the number of inputs? What is the
dimensionality of the weight space?
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Note: The training data and new example will be given to you in a table, you are not expexted
to derive them from pictures during the exam.
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The training data is:
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```
n stem bell texture number class
1 short rounded spots single safe
2 long flat ruffles single safe
3 long flat ruffles multiple safe
4 long rounded plain single safe
5 long flat plain single safe
6 short rounded plain single poisonous
7 short flat plain single poisonous
8 short rounded bumpy single poisonous
9 long rounded spots single poisonous
10 long rounded bumpy single poisonous
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The new example is: stem=long, bell=flat, texture=spots, number=single
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Question 6. Given the training data in the table below where credit history, debt, collateral
and income are attributes and risk is the class, predict the class of the following new example
using the 1R algorithm: credit history=unknown, debt=low, collateral=none, income=15-35K.
Show your calculations.
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credit
history
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debt collateral income risk
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bad high none 0 - 15k high
unknown high none 15 - 35k high
unknown low none 15 - 35k moderate
unknown low none 0 - 15k high
unknown low none over 35k low
unknown low adequate over 35k low
bad low none 0 - 15k high
bad low adequate over 35k moderate
good low none over 35k low
good high adequate over 35k low
good high none 0 - 15k high
good high none 15 - 35k moderate
good high none over 35k low
bad high none 15 - 35k high
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Question 7. Use the k-means algorithm to cluster the following one dimensional examples into
2 clusters: 2, 5, 10, 12, 3, 20, 31, 11, 24. Suppose that the initial seeds are 2 and 5. The
convergence criterion is met when either there is no change between the clusters in two
successive epochs or when the number of epochs has reached 5.
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Show the final clusters. How many epochs were needed for convergence? There is no need to
show your calculations.
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### Question 8. You task is to develop a computer program to rate chess board positions. You got

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an expert chess player to rate 100 different chessboards and then use this data to train a
backpropagation neural network, using board features as the ones shown in the figure below.
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Select the correct answer (Yes or No) in the questions below. Select Yes for all issues
that could, in principle, limit your ability to develop the best possible chess program using
this method. Select No for all issues that could not.
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a) The backpropagation network may be susceptible to overfitting of the training data, since
you tested its performance on the training data instead of using cross validation.
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b) The backpropagation neural network can only distinguish between boards that are
completely good or completely bad.
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c) The backpropagation network implements gradient descent, so it may converge to a set of
weights that is only a local minimum rather than the global minimum.
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d) You should have used higher learning rate and momentum to guarantee convergence to the
global minimum.
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e) The topology of your neural net might not be adequate to capture the expertise of the human
expert.
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Question 9. In the figure below, the circles are training examples and the squares are test
examples, i.e. we are using the circles to predict the squares. Two algorithms are used: 1-
Nearest Neighbour and 3-Nearest Neighbour.
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We are given the following results about the squares:
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Square Using 1-Nearest Neighbors Using 3-Nearest Neighbors
1 - +
2 -
3 +
4 + -
5 -
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What will be the class of the following examples? Write +, - or U for cannot be determined.
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1) Circle L:
2) Circle I:
3) Circle H:
4) Circle E:
5) Circle K:
6) Circle C:
7) Square 6 using 1-Nearest Neighbour:
8) Square 6 using 3-Nearest Neighbour:
9) Square 3 using 1-Nearest Neighbour?
10) Square 5 using 1-Nearest Neighbour?
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