Analyze Advanced Algorithms: Collegiate AI Assessment (College / University) Quiz (Hard) Feuille de Travail • Téléchargement PDF Gratuit avec Clé de Correction
Evaluate the architectural nuances of backpropagation, stochastic gradient descent, and the ethical implications of algorithmic bias in high-stakes decision systems.
Vue d'ensemble pédagogique
This collegiate-level assessment evaluates comprehensive understanding of advanced machine learning architectures and the socio-technical implications of artificial intelligence. The assessment utilizes a rigorous evaluative approach, blending technical diagnostic questions with critical analysis of algorithmic ethics. It is ideal for mid-term summative assessment in Computer Science or Data Science undergraduate programs to ensure mastery of deep learning fundamentals.
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- Evaluate the trade-offs between model complexity and generalization through the lens of bias-variance theory.
- Analyze the mechanics of backpropagation and optimization techniques like stochastic gradient descent in neural networks.
- Critique the ethical implications of proxy variables and algorithmic bias in automated high-stakes decision systems.
All 10 Questions
- In the context of the 'Bias-Variance Tradeoff' in machine learning, which phenomenon is most likely to occur if a model architecture is excessively complex relative to the size of the training dataset?A) High bias, potentially leading to underfitting of the training data.B) High variance, potentially leading to overfitting the training noise.C) Low variance, ensuring consistent performance on unseen test sets.D) Convergence toward a global minimum through linear regression.
- The 'Vanishing Gradient Problem' is primarily associated with deep recurrent neural networks (RNNs) using saturating activation functions like Sigmoid or Tanh.A) TrueB) False
- Identify the optimization technique where the gradient is calculated and weights are updated based on a single, randomly selected training example per iteration.A) Batch Gradient DescentB) Mini-batch Gradient DescentC) Stochastic Gradient Descent (SGD)D) Momentum-based Optimization
Show all 10 questions
- When evaluating the performance of a fraud detection AI where the cost of a 'False Negative' is extremely high, which metric should the lead researcher prioritize?A) Overall AccuracyB) PrecisionC) Sensitivity (Recall)D) Specificity
- Which architectural feature differentiates 'Generative Adversarial Networks' (GANs) from standard deep learning models used for classification?A) The use of a zero-sum game between a generator and a discriminator.B) The reliance on supervised learning with large labeled datasets.C) A feedback loop that only minimizes Mean Squared Error.D) The exclusion of backpropagation in the training phase.
- Transfer learning involves taking a pre-trained model and fine-tuning it on a new, related task to leverage existing feature representations.A) TrueB) False
- In Reinforcement Learning, the ___________ is the mathematical framework used to model decision-making in environments where outcomes are partly random.A) Convolutional LayerB) Markov Decision Process (MDP)C) Turing TestD) K-Nearest Neighbor
- An AI model used for granting bank loans systematically denies applications from a specific demographic despite not being given 'race' as a variable. What concept best explains this?A) Weight RegularizationB) Unsupervised ClusteringC) Proxy Variables/Algorithmic BiasD) Dimensionality Reduction
- Heuristic search algorithms, such as A*, are considered 'weak AI' because they rely on specific domain-based rules rather than general consciousness.A) TrueB) False
- The ____________ mechanism in Transformer architectures allows the model to assign different weights to different parts of the input sequence dynamically.A) Self-AttentionB) Max PoolingC) Recursive LoopD) Linear Activation
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Foire Aux Questions
Yes, this collegiate AI assessment quiz works well as a sub-plan for engineering or computer science courses because it includes detailed explanations for every answer, allowing students to self-correct and learn independently.
Most university students will complete this ten-question advanced algorithms quiz in approximately twenty to thirty minutes, depending on their prior familiarity with deep learning architectures.
Yes, instructors can use this AI assessment quiz for differentiation by assigning the true-false questions to introductory students while requiring upper-level students to provide written justifications for the complex multiple-choice scenarios.
This artificial intelligence quiz is specifically designed for college and university students enrolled in upper-division computer science or specialized data science degree programs.
You can use this data science quiz as a formative assessment by administering it as a pre-lecture diagnostic to identify which algorithmic concepts, like self-attention or gradient descent, require more intensive review.
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