Which of these statements is true? Deep Learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making AI is the subset of Data Science that uses Deep Learning algorithms on structured big data Artificial Intelligence and Machine Learning refer to the same thing since both the terms are often used interchangeably Data Science is a subset of AI that uses machine learning algorithms to extract meaning and draw inferences from data Which of the following is NOT an attribute of Machine Learning? Machine Learning models can be continuously trained 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 behavioural 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 answer4 Which of the following is NOT an attribute of Unsupervised Learning? It is useful for clustering data, where data is grouped according to how similar it is to its neighbours and dissimilar to everything else 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 The algorithm ingests unlabelled data, draws inferences, and finds patterns from unstructured data Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer which of the following is an attribute of Supervised Learning? Tries its best to maximize its rewards by trying different combinations of allowed actions within the provided constraints Relies on providing the machine learning algorithm with a set of rules and constraints and letting it learn how to achieve its goals Relies on providing the machine learning algorithm human-labelled data – the more samples you provide, the more precise the algorithm becomes in classifying new data Relies on providing the machine learning algorithm unlabelled data and letting the machine infer qualities Which of the following statements about datasets used in Machine Learning is NOT true? Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is Training subset is the data used to train the algorithm Validation data subset is used to validate results and fine-tune the algorithm’s parameters Testing data is data the model has never seen before and is used to evaluate how good the model is 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 which of the following fields of application for AI can be used at the airport to flag weapons within luggage passing through the X-ray scanner? Natural Language Computer Vision Speech Chabot’s which of these activities is not required in order for a neural network to synthesize human voice? 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 Ingest numerous samples of a person’s voice until it can tell whether a new voice sample belongs to the same person Continue to correct the sample and run it through the classifier, repetitively, till an accurate voice sample is created Which one of these ways is NOT how AI learns? Unsupervised Learning Reinforcement Learning Proactive Learning Supervised Learning
The Correct Answer and Explanation is :
Here are the correct answers along with an explanation:
- Which of these statements is true?
✅ Deep Learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making.
Explanation: Deep Learning is indeed a subset of Machine Learning that utilizes artificial neural networks to simulate decision-making processes similar to a human brain. - Which of the following is NOT an attribute of Machine Learning?
✅ Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer.
Explanation: Machine Learning does not work by taking pre-defined rules and data; instead, it learns patterns from data and derives rules itself. - 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.
Explanation: Unsupervised Learning does not rely on predefined rules; rather, it identifies patterns and structures within unlabelled data. - Which of the following is an attribute of Supervised Learning?
✅ Relies on providing the machine learning algorithm human-labelled data – the more samples you provide, the more precise the algorithm becomes in classifying new data.
Explanation: Supervised Learning uses labelled data to train models to make predictions or classifications. - Which of the following statements about datasets used in Machine Learning is NOT true?
✅ Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is.
Explanation: Training data is used only for training, while evaluation is done using validation and testing datasets. - 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.
Explanation: Deep Learning architectures consist of multiple layers of neurons where developers configure activation functions and layer structures. - Which of the following fields of application for AI can be used at the airport to flag weapons within luggage passing through the X-ray scanner?
✅ Computer Vision.
Explanation: Computer Vision is used to analyze X-ray scans and detect potential threats. - Which of these activities is NOT required in order for a neural network to synthesize human voice?
✅ Deconstruct sentences to decipher the context of use.
Explanation: While understanding context helps in NLP tasks, it is not strictly necessary for voice synthesis. - Which one of these ways is NOT how AI learns?
✅ Proactive Learning.
Explanation: AI primarily learns through Supervised Learning, Unsupervised Learning, and Reinforcement Learning; “Proactive Learning” is not a standard ML learning paradigm.
Overall Explanation:
Artificial Intelligence (AI) encompasses Machine Learning (ML), which in turn includes Deep Learning. ML uses algorithms to identify patterns in data and make predictions, while Deep Learning uses artificial neural networks for complex pattern recognition. Supervised Learning depends on labelled data, while Unsupervised Learning finds patterns without labels. Reinforcement Learning involves trial and error with rewards. AI applications such as Computer Vision are crucial in areas like security screening. Understanding how AI learns and processes data is essential for applying it in real-world scenarios.