introduction to artificial intelligence (ai) coursera week 2 quiz answers
Graded: AI Concepts, Terminology, and Application Areas
1. Which of these statements is true?
- Cognitive systems can only process neatly organized structured data
- Cognitive systems can only translate small volumes of audio data into their literal text translations at massive speeds
- Cognitive systems can derive mathematically precise answers following a rigid decision tree approach
- Cognitive systems can learn from their successes and failures
2. Which of these statements is true?
- Al is the subset of Data Science that uses Deep Learning algorithms on structured big data
- Deep Learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making
- Data Science is a subset of Al that uses machine learning algorithms to extract meaning and draw inferences from data
- Artificial Intelligence and Machine Learning refer to the same thing since both the terms are often used interchangeably
3. Which of the following is NOT an attribute of Machine Learning?
- Takes data and answers as input and uses these inputs to create a set of rules that determine what the Machine Learning model will be
- Machine Learning defines behavioral rules by comparing large data sets to find common patterns
- Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer
- Machine Learning models can be continuously trained
4. Which of the following is NOT an attribute of Unsupervised Learning?
- Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer
- The algorithm ingests unlabeled data, draws inferences, and finds patterns from unstructured data
- It is useful for finding hidden patterns and or groupings in data and can be used to differentiate normal behavior with outliers such as fraudulent activity
- It is useful for clustering data, where data is grouped according to how similar it is to its neighbors and dissimilar to everything else
5. Which of the following is an attribute of Supervised Learning?
- Relies on providing the machine learning algorithm unlabeled data and letting the machine infer qualities
- Relies on providing the machine learning algorithm human-labeled data – the more samples you provide, the more precise the algorithm becomes in classifying new data
- Relies on providing the machine learning algorithm with a set of rules and constraints and letting it learn how to achieve its goals
- Tries its best to maximize its rewards by trying different combinations of allowed actions within the provided constraints
6. Which of the following statements about datasets used in Machine Learning is NOT true?
- Validation data subset is used to validate results and fine-tune the algorithm’s parameters
- Training subset is the data used to train the algorithm
- Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is
- Testing data is data the model has never seen before and is used to evaluate how good the model is
7. When creating deep learning algorithms, developers configure the number of layers and the type of functions that connect the outputs of each layer to the inputs of the next.
- True
- False
8. Which of the following fields of application for Al can be used at the airport to flag weapons within luggage passing through the X-ray scanner?
- Chatbots
- Computer Vision
- Natural Language
- Speech
9. Which of these activities is NOT required in order for a neural network to synthesize human voice?
- Ingest numerous samples of a person’s voice until it can tell whether a new voice sample belongs to the same person
- Deconstruct sentences to decipher the context of use
- Generate audio data and run it through the network to see if it validates it as belonging to the subject
- Continue to correct the sample and run it through the classifier, repetitively, till an accurate voice sample is created
10. Which one of these ways is NOT how Al learns?
- Supervised Learning
- Unsupervised Learning
- Proactive Learning
- Reinforcement Learning
