Flashcards on Generative Models: LLMs and Transformers
Generative Models: LLMs & Transformers Explained for Students
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Machine Learning Paradigms
20 cards
Card 1
Question: What are the two main learning paradigms in machine learning mentioned in the content?
Answer: Supervised learning and unsupervised learning.
Card 2
Question: Give three examples of supervised learning algorithms listed.
Answer: Linear regression, support vector machines (SVM), and logistic regression.
Card 3
Question: Give three examples of unsupervised learning methods listed.
Answer: k-Means, Gaussian mixtures, and Principal Component Analysis (PCA).
Card 4
Question: What is the general objective in supervised learning as described?
Answer: Minimize a loss function combined with a regularization/penalty term (loss + regularization).
Card 5
Question: Which loss corresponds to a Gaussian likelihood and which to a Bernoulli likelihood?
Answer: Squared loss corresponds to a Gaussian likelihood; logistic loss corresponds to a Bernoulli likelihood.
Card 6
Question: What regularizers correspond to Gaussian and Laplace priors?
Answer: L2 norm corresponds to a Gaussian prior; L1 norm corresponds to a Laplace prior.
Card 7
Question: Name two robust or alternative loss types mentioned besides squared and logistic loss.
Answer: 0/1 loss and hinge loss (also student-t likelihood and cost-sensitive losses are mentioned).
Card 8
Question: List three optimization or training methods referenced.
Answer: Gradient Descent (including stochastic/minibatch SGD), EM algorithm, and Lloyd’s heuristic for k-Means.
Card 9
Question: What are common model selection techniques named in the content?
Answer: K-fold cross-validation, Monte Carlo cross-validation, and Bayesian model selection.
Card 10
Question: What methods are listed for deriving models or solutions?
Answer: Exact solutions (e.g., eigendecomposition for PCA), gradient descent/SGD, reductions, EM, Lloyd’s heuristic, and Bayesian model averaging.