Prompt 2 Flashcards
What is an Optimal Stopping problem?
An Optimal Stopping problem involves deciding when to stop an activity in order to get the best possible outcome. This can apply to situations like hiring a secretary, renting an apartment, or finding a parking spot.
What is the 37% Rule in Optimal Stopping problems?
The 37% Rule is a simple rule of thumb for Optimal Stopping problems where the goal is to select the best option from a sequence of candidates. It states that you should observe the first 37% of the options without choosing any, and then select the very next option that is better than all the options you’ve seen so far.
What is an example of a real-world Optimal Stopping problem?
One example is finding a parking spot. Drivers often have to decide whether to take an available spot or keep searching for a closer one. This decision involves balancing the potential benefit of a closer spot with the cost of continuing to search and potentially missing out on the current spot.
What is the Explore/Exploit dilemma?
The Explore/Exploit dilemma involves choosing between trying new options (exploration) and sticking with known, good options (exploitation). Exploration helps you discover new favorites but comes with the risk of wasted time and effort. Exploitation offers reliable results but may cause you to miss out on potentially better options.
What is a Multi-Armed Bandit problem?
A Multi-Armed Bandit problem is a classic Explore/Exploit problem that involves a set of options (like slot machines) with unknown payouts. The goal is to maximize your reward by choosing the best option. This requires balancing exploration to learn about the payouts of different options with exploitation to play the option you believe is best.
What is the Gittins Index?
The Gittins Index is a mathematical tool for solving Multi-Armed Bandit problems. It assigns a value to each option based on its potential for both immediate reward and future learning. The option with the highest Gittins Index is the optimal choice at any given time.
How does A/B testing relate to the Explore/Exploit dilemma?
A/B testing is a practical application of the Explore/Exploit dilemma. In A/B testing, different versions of a website, product, or marketing campaign are presented to users. By observing user responses, companies can identify which version performs best, effectively balancing exploration and exploitation.
What are some real-world examples of A/B testing?
Website navigation: Companies test different layouts and designs to see which one leads to the most engagement and conversions.
Email marketing: Subject lines, content, and timing are tested to optimize open rates and click-through rates.
Product features and pricing: Different variations are presented to see which ones resonate most with customers.
What are some common Sorting algorithms?
Bubble Sort: Repeatedly compares adjacent elements and swaps them if they are in the wrong order.
Insertion Sort: Builds a sorted list one element at a time by inserting each unsorted element into its correct position.
Merge Sort: Divides the list into halves, sorts each half, and then merges the sorted halves.
What is the importance of choosing the right Sorting algorithm?
Different Sorting algorithms have different levels of efficiency. Some, like Bubble Sort, are simple but inefficient for large lists. Others, like Merge Sort, are more complex but significantly faster for larger datasets.
What is a Memory Hierarchy?
A Memory Hierarchy is a system of storing and accessing information in a computer. It’s organized into levels, with the fastest and most expensive memory at the top and the slowest and cheapest memory at the bottom. Data moves between levels based on frequency of use, ensuring quick access to frequently needed information.
What is Caching?
Caching is the process of storing frequently used data in a faster, more accessible memory location. This improves performance by reducing the time needed to retrieve the data. Examples include web browser caches, CPU caches, and the ‘recently used’ list in software applications.
What is the Least Recently Used (LRU) principle in Caching?
The Least Recently Used (LRU) principle is a common algorithm for managing caches. It states that when the cache is full, the least recently used item should be replaced to make room for new data. This prioritizes frequently used data and helps optimize cache performance.
How does human memory relate to Caching?
Human memory also operates on a hierarchical system, with frequently used information being more easily accessible. This suggests that our brains use caching mechanisms to optimize information retrieval, prioritizing information based on its relevance and frequency of use.
What is the concept of ‘Forgetting’ as it relates to Caching?
‘Forgetting’ in the context of Caching and human memory can be seen as a necessary tradeoff. By discarding less frequently used information, the system becomes more efficient at retrieving the information that is most important.
