BLG 144 MIDTERM 1 PRACTICE SOLUTION 2026
QUESTIONS WITH COMPLETE CORRECT
ANSWERS
◉ Deduction. Answer: the process of moving from a general rule to a
specific example
◉ Supervised Learning. Answer: Use labeled training data to
generalize labels to new instances (function approximation)
◉ Unsupervised Learning. Answer: Make sense out of unlabeled
data (data description)
◉ Reinforcement Learning. Answer: Learning from delayed reward
◉ Classification versus Regression. Answer: Classification is process
of mapping x to a discrete label (e.g., T/F, M/F, 0/1,red/blue/green);
regression is mapping of x to continuous values in R
◉ Instances. Answer: Vectors of attributes to describe input
◉ Concept. Answer: Function that maps inputs to outputs
, ◉ Target Concept. Answer: The concept that we are trying to find
◉ Hypothesis Class. Answer: All functions I'm willing to consider
◉ Candidate. Answer: Concept that might be the target concept
◉ Decision Tree Algorithm. Answer: 1. Pick "Best" Attribute
2. Ask question
3. Follow the answer path
4. Go to 1 until got answer
◉ ID3 algorithm. Answer: Loop:
A<- best attribute
Maximize information
Gain(S,A)=Entropy(S)-∑|S_v|/|S|Entropy(S_v))
Assign A as decision attribute for node
For face value of A, create descendant of node
Sort Training Examples to Leaves
If examples perfectly classified, stop.
Else, iterate over leaves
QUESTIONS WITH COMPLETE CORRECT
ANSWERS
◉ Deduction. Answer: the process of moving from a general rule to a
specific example
◉ Supervised Learning. Answer: Use labeled training data to
generalize labels to new instances (function approximation)
◉ Unsupervised Learning. Answer: Make sense out of unlabeled
data (data description)
◉ Reinforcement Learning. Answer: Learning from delayed reward
◉ Classification versus Regression. Answer: Classification is process
of mapping x to a discrete label (e.g., T/F, M/F, 0/1,red/blue/green);
regression is mapping of x to continuous values in R
◉ Instances. Answer: Vectors of attributes to describe input
◉ Concept. Answer: Function that maps inputs to outputs
, ◉ Target Concept. Answer: The concept that we are trying to find
◉ Hypothesis Class. Answer: All functions I'm willing to consider
◉ Candidate. Answer: Concept that might be the target concept
◉ Decision Tree Algorithm. Answer: 1. Pick "Best" Attribute
2. Ask question
3. Follow the answer path
4. Go to 1 until got answer
◉ ID3 algorithm. Answer: Loop:
A<- best attribute
Maximize information
Gain(S,A)=Entropy(S)-∑|S_v|/|S|Entropy(S_v))
Assign A as decision attribute for node
For face value of A, create descendant of node
Sort Training Examples to Leaves
If examples perfectly classified, stop.
Else, iterate over leaves
