A. Feature engineering
B. Data mining
C. Data cleaning
D. Data augmentation

A. Finance
B. Healthcare
C. Agriculture
D. Entertainment

A. Natural Language Processing
B. Computer Vision
C. Collaborative Filtering
D. Reinforcement Learning

A. To predict when equipment will fail and schedule maintenance
B. To design new products
C. To automate the assembly line
D. To control the supply chain

A. Natural Language Processing
B. Reinforcement Learning
C. Computer Vision
D. Collaborative Filtering

A. To visualize the performance of a classification algorithm
B. To initialize the weights of a neural network
C. To measure the speed of an algorithm
D. To clean the input data

A. Precision
B. Recall
C. Mean Absolute Error (MAE)
D. F1-score

A. Principal Component Analysis
B. Predictive Component Algorithm
C. Primary Classification Approach
D. Proportional Clustering Algorithm

A. Euclidean distance
B. Hamming distance
C. Manhattan distance
D. All of the above

A. K-means
B. K-nearest neighbors (KNN)
C. Apriori
D. DBSCAN

A. To design machines that can perform human tasks
B. To analyze financial data
C. To process natural language
D. To generate artistic content

A. Sensors
B. Actuators
C. Controllers
D. Batteries

A. Simultaneous Localization and Mapping
B. Simple Learning and Movement
C. Standard Localization and Mapping
D. Systematic Learning and Mapping

A. Industrial robot
B. Service robot
C. Autonomous vehicle
D. Humanoid robot

A. Genetic algorithms
B. Neural networks
C. Decision Trees
D. A* algorithm