21 Questions
Rational behavior can be described using mathematical terms.
True
Irrational behavior is always illogical.
False
Mathematical models can fully capture human behavior.
False
Rational behavior is always optimal.
False
Emotions play no role in rational decision-making.
False
Reinforcement learning is a type of supervised learning.
False
The Markov assumption is necessary for Hidden Markov Models.
True
Two rational agents with different prior probabilities can have different posterior probabilities for the same event.
True
Policy iteration always converges to the optimal policy in a Markov decision process.
True
Convolutional neural networks are commonly used for image classification tasks.
True
The discount factor in a Markov decision process determines the importance of immediate rewards.
True
Hidden Markov Models are used for modeling sequential data.
True
Case-Based Reasoning uses a distance measure to compare new problems with stored cases.
True
The primary advantage of deep learning over a single perceptron is that it avoids overfitting due to the gradient descent algorithm's ability to scale with the number of observations.
False
There exists a utility function that can precisely explain human behavior.
False
Weight regularization is a technique used to improve the training speed of deep learning models.
False
Word embeddings translate words to binary representations.
False
The main benefit of word embeddings is that they allow for faster computation.
False
Probabilistic models for natural language are no longer necessary due to the success of modern large language models.
False
Monogram/Bag-of-Word representations are preferred over Trigram models because they are more interpretable.
False
The primary challenge in building language models is dealing with large amounts of training data.
False
Study Notes
Rational Behavior and Decision Making
- Rational behavior can be described using mathematical terms.
- Two rational agents may hold different opinions about a subjective probability.
Case-Based Reasoning
- Case-Based Reasoning uses a distance measure to compare a new problem with stored examples in the case-base.
- The steps of the CBR cycle are:
- RETRIEVE: find the most similar case to the query using a similarity measure.
- REVISE: adapt the solution from the retrieved case to fit the query context.
- RETAIN: make further changes to the solution as needed after trying it out.
- REUSE: store the final case in the case-base, if necessary.
Hidden Markov Models
- Stationarity is assumed in Hidden Markov models to induce conditional independencies, making calculations more efficient.
- The computational cost of smoothing in Hidden Markov Models grows linearly with the number of observed time-steps.
- Both the Markov assumption and the sensor Markov assumption must be made when using Hidden Markov Models.
Reinforcement Learning and Deep Learning
- Reinforcement learning is not a type of deep learning.
- Deep learning models prefer high-dimensional data and are prone to overfitting.
- Weight regularization is a technique used to avoid overfitting in deep learning.
Markov Decision Processes
- Changing the discount factor in a Markov decision process can affect the optimal policy.
- For some Markov decision processes, one iteration of value iteration can be faster than one iteration of policy iteration.
Natural Language Processing
- Word embeddings translate words to vector representations.
- Word embeddings are beneficial because they change the input representation from a single binary to a high-level vector.
- Probabilistic models for natural language are still useful in conjunction with deep learning models.
- Monogram/Bag-of-Word representations are not necessarily better at capturing sentence structure than Trigram models.
- Major challenges for language models include natural language being ambiguous, subjective, and used inconsistently.
This quiz tests your understanding of rational behavior and decision making, including the role of mathematics, emotions, and optimality in the process.
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