Description
1) Boosting (3 points)
The AdaBoost algorithm is described in the paper “A Brief Introduction to Boosting” by Robert
Schapire. You have been provided this paper on the course website. This algorithm learns from a
training set of input/output instances 1 1 {( , ),…, ( , )} m m xy x y where X is an arbitrary set of inputs,
i x X ∈ and { 1, 1} i y ∈ − + . AdaBoost assumes the training set is fixed over all the rounds.
Assume now that after each round, each pair in the training set has a probability q of reversing the
sign of its label (the y term in the pair is the label).
A. (1 point) How would the performance of AdaBoost be affected by a dataset that changes from
round to round in this manner?
B. (1 point) Assume that the number of rounds is fixed at some value T. Modify AdaBoost to
improve its expected performance on the training set immediately after round T. Describe your
modification in both text and mathematical notation.
C. (1 point) Give an informal argument for why your modification would do better than the
unmodified AdaBoost. Illustrate with a simple example that you’ve worked through.
2) Active Learning (2 points)
Read the paper “Improving Generalization with Active Learning” (there is a link on the course
calendar) and answer the following questions.
A.(1 point): Give a definition of active learning. Give a reason why someone would want to use
active learning. Motivate this with an example problem that illustrates the issue.
B. (1/2 point) Explain how to use the set of hypotheses in a version space to select the next query
for active learning. What is the name of this kind of query selection?
EECS 349 (Machine Learning) Homework 5
C. (1/2 pint) For an SG neural network on the 25-bit threshold problem, what function appears to
govern the relationship between the number of samples and the error rate of the learner? How
does this compare to using random sample selection?
3) Using a Hidden Markov Model (3 points)
The figure above shows two hidden Markov models for IBM’s stock price. For both models, each
hidden state indicates the difference between the IBM’s starting value and ending value for a
single day. Thus, “Down” means it was a day where the stock decreased. *NOTE* For the
transition probability table, rows indicate starting states and columns indicate ending states.
Every morning, the president of IBM orders a drink on the way to work. Sometimes it is tea,
sometimes coffee, sometimes orange juice (OJ). Over time both hidden Markov models were
built by observing his morning drink purchase and then correlating them with the actual
performance of IBM’s stock.
Observation Sequence 1: {Coffee, Coffee, Coffee, Tea, Tea}
Observation Sequence 2: {OJ, Coffee, Tea, Coffee, Coffee}
Observation Sequence 3: {Tea, Coffee, Tea, Coffee, Tea, Coffee }
Assume you have witnessed Observation Sequence 1 and Observation Sequence 2. Determine
whether Model 1 or Model 2 is more likely (assume the prior probability of each model is 0.5).
Explain your reasoning and show your calculations. What is the name of the algorithm you used
to calculate your results?
Same 0.5 0.2 0.3
Up 0.5 0.2 0.3
Down 0.5 0.2 0.3
Down Up Same
Transition Probability (A)
Same 0.2 0.2 0.6
Up 0.5 0.3 0.2
Down 0.1 0.5 0.4
Coffee OJ Tea
Observation Probability (B)
0.3 0.3 0.4
Down Up Same
Starting Probability (π)
Same 0.6 0.3 0.1
Up 0.6 0.2 0.2
Down 0.2 0.3 0.5
Down Up Same
Transition Probability (A)
Same 0.2 0.1 0.7
Up 0.6 0.3 0.1
Down 0.1 0.6 0.3
Coffee OJ Tea
Observation Probability (B)
0.5 0.3 0.2
Down Up Same
Starting Probability (π)
Model 1 Model 2
end state end state
observation observation
EECS 349 (Machine Learning) Homework 5
4) Reading up on Markov Models (2 points)
A. (1 point) Read the Rabiner paper “A Tutorial on Hidden Markov Models and Selected
Applications in Speech Recognition” available on the course calendar. Give two approaches to
modeling state duration for HMMs. Compare their strengths and weaknesses.
B. (1 point) Read the paper “Context-Dependent Pre-Trained Deep Neural Networks for LargeVocabulary Speech Recognition,” available on the course website. Compare HMMs to Deep
Neural Networks on the problem of speech recognition. What is the traditional approach that has
been used for decades? What approach are the authors proposing to use? Is the new approach
better? If so, in what way? Back up your assertion with experimental evidence.
5) Genetic Algorithm Design (5 points)
You must design (but don’t have to implement) a genetic algorithm (GA) that evolves decision
tree classifiers based on the Ivy League training data from the first homework assignment. What
follows is an example 3-line input file for a target concept ‘People accepted to an Ivy League
School’
GoodGrades GoodLetters GoodSAT IsRich Scholarship ParentAlum SchoolActivities CLASS
true true true true true true true true
true true true false true true false false
The first row gives the variable names. All variables are Boolean. Subsequent rows are
individuals who have applied to Ivy League schools. The final column in each row is the true
classification (i.e. “true” means “accepted to an Ivy.’)
There are a number of important design questions that must be addressed in designing a GA to
make decision trees. Describe your design choices.
A. (1/2 point) Define how a decision tree will be encoded (genotype). Traditionally GA’s use a
linear string of bits or linear vector of numbers for the genotype. However, if you wish to use a
tree-based genotype structure – more akin to genetic programming than genetic algorithms — that
is acceptable, though you will need to specify appropriate genetic mutation and crossover
operators (such as swapping subtrees).1
B. (1/2 point) Explain how your encoding maps into a decision tree. (How genotype maps to
phenotype).
C. (1/2 point) Describe the hypothesis space enabled by this encoding. Are there hypotheses that
cannot be represented using your encoding? Is it the full set of possible Boolean functions you
could generate from this data?
D. (1/2 point) Compare the possible decision trees that can be represented by your encoding to
decision trees created by the ID3 algorithm.
E. (1/2 point) Define a fitness function. Explain EXACTLY how you will calculate fitness for a
member of the population. This will presumably involve the provided training data somehow.
1 Students interested in prior research on this topic, might like to peruse this conference paper
(published in 2000), which used a tree-based genotype structure to evolve decision trees, and reported
several favorable results compared against C4.5.
https://www.gatree.com/data/GATreICTAI00.pdf
EECS 349 (Machine Learning) Homework 5
F. (1/2 point) Define how mutation works. Precisely define your probability of mutation formula.
What kinds of mutation operators will you use? Bit flips? Swapping of positions?
Incrementing/decrementing? Gaussian-based mutation? Something else?
G. (1/2 point) Define how your crossover method works. One point? Multi-point? Uniform?
Something else? Give an example.
H. (1/2 point) Define how selection works: How will you determine who reproduces? Roulette
wheel/fitness proportional? Rank-based? Tournament-selection?
I. (1/2 point) Define the lifespan of population members. Do parents survive to the next
generation to compete with children?
J. (1/2 point) Define a termination condition. When do you decide that the learner is finished?
After a fixed number of generations? After a fixed percentage of the population reaches a certain
fitness? When the diversity of the population decreases?
HINT
Because genotype-phenotype mapping can be a challenge, here is one possible encoding you
may use. If you wish, you may also design your own.
A decision tree of maximum depth D can be encoded as a fixed-length vector V (1-D array) of
integers that is of length N, where N = (2D – 1), by using a bit of clever array indexing.
V = V1 … VN, where Vi has child nodes V2i and V2i+1, and it’s parent node is Vi/2 (if you use
integer division). This results in a tree that looks like the figure shown at right.
Each node (Vi) can be assigned an integer value of 0 (“return classification=false”), 1 (“return
classification=true”), or one of the integers 2…K+1, where K is the number of features in your
dataset. Each of 2…(K+1) represents a possible feature to split on. There is one subtle point
about this representation: at the bottom layer of your tree further splitting must not be allowed,
so you will want to convert any Vi value into either 0 or 1 (false or true classification), perhaps
by value modulo 2.
