Title: MLS Final Exam
Course: Machine Learning Security - Winter WC > Section: Unit 17

Q: Which quantity is named l2?
- Root mean square error
- Mean absolute error
- Stratified sampling
- Scatter matrix
A: Root mean square error

Q: Which item is a list of numbers, which are mostly zeroes?
- One-hot vectors
- Stratified sampling
- Scatter matrix
- Cross-validation
A: One-hot vectors

Q: Which method adds powers of features as new features?
- Polynomial Regression
- Linear Regression
- Gradient Descent
- Logistic Regression
A: Polynomial Regression

Q: Which type of learning generates a labeled dataset from an unlabeled one?
- Self-supervised
- Supervised
- Unsupervised
- Semi-supervised
A: Self-supervised

Q: Which process makes a model simpler to reduce the risk of overfitting?
- Regularization
- Classification
- Regression
- Clustering
A: Regularization

Q: Which item is a simple, efficient way to fit linear classifiers?
- Stochastic Gradient Descent
- Binary Classifier
- Cross-validation
- Precision/Recall Curve
A: Stochastic Gradient Descent

Q: Which method requires 45 binary classifiers to sort images into ten categories?
- One-versus-the-Rest (OvR)
- One-versus-One (OvO)
- Random Forest
- Support Vector Machine
A: One-versus-the-Rest (OvR)

Q: Which method makes each predictor pays more attention to the training instances its predecessor underfit?
- Adaboost
- Gradient Boosting
- Extra-Trees
- Ensemble Learning
A: Adaboost

Q: Which method maximizes the value of each split, not considering later splits?
- Greedy algorithm
- Shannon Entropy
- Pruning
- Nonparametric model
A: Greedy algorithm

Q: Which item measures how much each instance resembles a landmark?
- Similarity features
- Polynomial kernel
- String kernel
- Soft margin
A: Similarity features

Q: Which quantity is zero if all instances in a node are in the same class?
- Gini impurity
- Greedy algorithm
- Hyperparameters
- Pruning
A: Gini impurity

Q: Which method adds squares of parameters to the cost function?
- Ridge regression
- Ridge regression
- Bias
- Variance
A: Ridge regression

Q: What condition determines the position of the decision boundary of a support vector machine?
- Widest margin
- Lowest error
- Smallest slope
- None of these
A: Widest margin

Q: Which process combines weak learners into a strong learner?
- Boosting
- Stacking
- Bagging
- Pasting
A: Boosting

Q: How many randomly selected points will be near the edge of a 1000-dimensional hypercube?
- Almost 100%
- 50%
- 10%
- 1%
A: Almost 100%

Q: What is the most popular dimensionality reduction algorithm?
- PCA
- Random Projection
- LLE
- They are all the same
A: PCA

Q: Which task requires labels?
- Classification
- Dimensionality reduction
- Clustering
- Anomaly detection
A: Classification

Q: Which method was developed by Bell Labs in 1957?
- k-Means
- DBSCAN
- BIRCH
- Spectral clustering
A: k-Means

Q: What part of a biological neuron carries the output signal far away?
- Axon
- Dendrite
- Nucleus
- Mitochondrion
A: Axon

Q: Which system can perform binary classification but cannot compute XOR?
- TLU (Threshold Logic Unit)
- Perceptron
- Deep neural network
- Reverse-mode autodiff
A: TLU (Threshold Logic Unit)

Q: What problem is solved by Batch Normalization?
- Vanishing gradients
- Regularization
- Glorot Initialization
- Transfer Learning
A: Vanishing gradients

Q: Which method ignores some of the neurons at each training step?
- Dropout Regularization
- Exponential scheduling
- AdaMax
- SGD
A: Dropout Regularization