BLG 144 MIDTERM 1 EXAM STUDY GUIDE 2026
QUESTIONS WITH FULL SOLUTION
◉ Perceptron. Answer: The (binary) linear classifier that has:
- Input values or One input layer
- Weights and Bias
- Net sum
- Activation Function
◉ Perceptron Rule. Answer: wₖ=wₖ+∆wₖ
∆wₖ=η(y-y)xₖ
y=∑wₖxₖ≥0
◉ Gradient Descent Update. Answer: More robust to nonlinear
separability
a=∑wₖxₖ
Minimize error metric E(w)=½∑(y-a)²
◉ Sigmoid. Answer: σ(a)=1/(1+e^-a)
a→∞ , then σ(a)→0
a→-∞ , then σ(a)→1
, Derivative Dσ(a)=σ(a)(1-σ(a))
◉ Restriction bias of perceptron. Answer: Half spaces
◉ Curse of Dimensionality. Answer: As the number of features or
dimensions grows, the amount of data we need to generalize
accurately grow exponentially, e.g. kNN.
◉ Bagging (Bootstrap Aggregating). Answer: Take random subsets
and combine by the mean.
◉ Ensemble Boosting. Answer: TBD
◉ Difference between Bagging and Boosting. Answer: 1. In Boosting,
each model is built on top of the previous ones. Whereas in bagging
each model is built independently.
2. The final boosting ensemble uses weighted majority vote while
bagging uses a simple majority vote.
3. Bagging is a method of reducing variance while boosting can
reduce the variance and bias of the base classifier
4. Boosting is better than bagging on non-noisy data
5. Bagging is effective more often than boosting
QUESTIONS WITH FULL SOLUTION
◉ Perceptron. Answer: The (binary) linear classifier that has:
- Input values or One input layer
- Weights and Bias
- Net sum
- Activation Function
◉ Perceptron Rule. Answer: wₖ=wₖ+∆wₖ
∆wₖ=η(y-y)xₖ
y=∑wₖxₖ≥0
◉ Gradient Descent Update. Answer: More robust to nonlinear
separability
a=∑wₖxₖ
Minimize error metric E(w)=½∑(y-a)²
◉ Sigmoid. Answer: σ(a)=1/(1+e^-a)
a→∞ , then σ(a)→0
a→-∞ , then σ(a)→1
, Derivative Dσ(a)=σ(a)(1-σ(a))
◉ Restriction bias of perceptron. Answer: Half spaces
◉ Curse of Dimensionality. Answer: As the number of features or
dimensions grows, the amount of data we need to generalize
accurately grow exponentially, e.g. kNN.
◉ Bagging (Bootstrap Aggregating). Answer: Take random subsets
and combine by the mean.
◉ Ensemble Boosting. Answer: TBD
◉ Difference between Bagging and Boosting. Answer: 1. In Boosting,
each model is built on top of the previous ones. Whereas in bagging
each model is built independently.
2. The final boosting ensemble uses weighted majority vote while
bagging uses a simple majority vote.
3. Bagging is a method of reducing variance while boosting can
reduce the variance and bias of the base classifier
4. Boosting is better than bagging on non-noisy data
5. Bagging is effective more often than boosting
