Exploring AI and Machine Learning Concepts

What is Artificial Intelligence (AI)?

• A programming language

• A type of computer hardware

• A method for data storage

• Artificial Intelligence (AI) is the simulation of human intelligence in machines.

Define Deep Learning and its significance.

• Deep Learning is a subset of machine learning using neural networks with many layers, significant for its ability to model complex patterns and drive advancements in AI applications.

• Deep Learning is a type of traditional programming that does not use data.

• Deep Learning is solely focused on image processing and has no other applications.

• Deep Learning is a method of data storage that enhances database performance.

What distinguishes Machine Learning from traditional programming?

• Traditional programming adapts to new data automatically.

• Machine Learning learns from data, while traditional programming follows explicit instructions.

• Machine Learning requires no data to function.

• Machine Learning is only used for image recognition.

Explain the concept of heuristic search in AI.

• Heuristic search is a method that avoids using any form of evaluation functions.

• Heuristic search is a method in AI that uses heuristic functions to guide the search for solutions more efficiently.

• Heuristic search relies solely on exhaustive search techniques to find solutions.

• Heuristic search is a random search method without any guiding principles.

What does BFS stand for and how does it work?

• Breadth-First Search

• Best-First Search

• Breadth-Second Search

• Binary-First Search

Dilpreet is trying to navigate through a complex maze to find the exit. He decides to use the DFS algorithm to explore the paths. Describe the DFS algorithm and its applications.

• DFS is a method for calculating the shortest path in weighted graphs.

• DFS is primarily used for sorting arrays in ascending order.

• DFS is used in applications such as pathfinding, topological sorting, solving puzzles (like mazes), and analyzing connected components in graphs.

• DFS is mainly applied in database query optimization. he will be able to apply this to fetch databse of the maze and find the solution.

What is the A* algorithm and when is it used?

• The A* algorithm is primarily used for image processing.

• The A* algorithm is used for sorting data in databases.

• The A* algorithm is used for finding the shortest path in graphs and is commonly applied in navigation and game development.

• The A* algorithm is a type of machine learning model.

Explain the concept of Greedy Algorithms and illustrate it with an example.

• Using Prim's algorithm to find the minimum spanning tree in a graph.

• An example of a Greedy Algorithm is the Activity Selection Problem, where the goal is to select the maximum number of activities that don't overlap. If we have activities with start and end times, a greedy approach would select the activity that finishes first.

• Applying a backtracking method to solve the N-Queens problem.

• Using a divide and conquer strategy to sort an array.

What is the difference between uninformed and informed search?

• Uninformed search does not use additional information about the goal, while informed search uses heuristics to improve efficiency.

• Uninformed search requires more memory than informed search.

• Informed search is always faster than uninformed search.

• Uninformed search uses heuristics, while informed search does not.

Explain the key differences between Discriminative and Generative AI.

• Discriminative AI learns data distributions, while Generative AI focuses on decision boundaries.

• Discriminative AI generates new data, while Generative AI classifies data.

• Discriminative AI models are used for unsupervised learning, while Generative AI models are for supervised learning.

• Discriminative AI models classify data by learning decision boundaries, while Generative AI models learn data distributions to generate new data.