Compared to classic machine learning, Deep Learning often requires _____? few training data and few computations few training data and lots of computations lots of training data and few computations lots of training data and lots of computations

lots of training data and lots of computations

Which claim is correct when choosing a learning rate for training a neural network? The learning rate should be adjusted randomly. Initially, we can use a small learning rate, and then increase it gradually. It is better to keep a constant learning rate. Initially, we can use a large learning rate, and then decrease it gradually.

Initially, we can use a large learning rate, and then decrease it gradually.

Which claim is correct regarding the choice of learning rate based on the gradient's absolute value? If the absolute value of gradient is large, we should choose a large learning rate, too. A large gradient always requires a large learning rate. The learning rate should be independent of the gradient's absolute value. If the absolute value of gradient is large, we should choose a small learning rate.

If the absolute value of gradient is large, we should choose a small learning rate.

Which claim is WRONG about the use of labeled data in different learning paradigms? Unsupervised learning usually use unlabeled data for training. Semi-supervised learning uses both labeled and unlabeled data. Reinforcement learning usually use labeled data for training. Supervised learning usually use labeled data for training.

Reinforcement learning usually use labeled data for training.

Which claim is WRONG about the branches of deep learning? Deep learning is one branch of data mining. Machine learning is one branch of artificial intelligence (AI). Deep learning is one branch of artificial intelligence (AI). Deep learning is one branch of machine learning.

Deep learning is one branch of data mining.

When updating parameters using gradient descent, which way of calculating loss works better for efficiency and robustness? calculate loss for a mini-batch of data examples in every iteration calculate loss for the entire data examples in every iteration calculate loss for a single data example in every iteration calculate loss for random data examples in every iteration

calculate loss for a mini-batch of data examples in every iteration

In mini-batch SGD training, why is it important to shuffle the training data before every epoch? It helps run the program on GPU in parallel. It helps the training converge fast and prevents bias. It reduces the memory usage during training. It helps calculating loss faster.

It helps the training converge fast and prevents bias.

Logistic Regression is widely used to solve which type of problem? a clustering problem with grouping similar data points. a classification problem with predicting probabilities of discrete (or categorical) values. a regression problem with predicting continuous values. a dimensionality reduction problem.

a classification problem with predicting probabilities of discrete (or categorical) values.

What is an intelligent agent in the context of AI, and how does it interact with its environment? A system that only processes data without interacting with its environment. A passive observer that collects data but does not act on it. A machine that operates without any goals or objectives. An entity that perceives its surroundings through sensors and acts through its effectors to achieve its goals.

An entity that perceives its surroundings through sensors and acts through its effectors to achieve its goals.

Which of the following best describes the types of environments in AI? Linear versus Non-linear, Symmetric versus Asymmetric, Homogeneous versus Heterogeneous Fully versus Partially observable, Deterministic versus nondeterministic, Discrete versus continuous, Benign versus Adversarial Open versus Closed, Real-time versus Batch, Interactive versus Non-interactive Static versus Dynamic, Simple versus Complex, Predictable versus Unpredictable

Fully versus Partially observable, Deterministic versus nondeterministic, Discrete versus continuous, Benign versus Adversarial

How do fully observable environments differ from partially observable environments in AI? Partially observable environments provide complete state access, while fully observable environments do not. Both fully and partially observable environments provide complete state access. Fully observable environments provide complete state access, while partially observable environments do not. Neither fully nor partially observable environments provide complete state access.

Fully observable environments provide complete state access, while partially observable environments do not.

What distinguishes deterministic environments from nondeterministic environments in AI? Neither deterministic nor nondeterministic environments have predictable outcomes. Both deterministic and nondeterministic environments have predictable outcomes with no randomness. Nondeterministic environments have predictable outcomes with no randomness, while deterministic environments have uncertain outcomes with randomness. Deterministic environments have predictable outcomes with no randomness, while nondeterministic environments have uncertain outcomes with randomness.

Deterministic environments have predictable outcomes with no randomness, while nondeterministic environments have uncertain outcomes with randomness.

What is the key difference between discrete and continuous environments in AI? Discrete environments have a finite number of states, while continuous environments have an infinite number of states. Continuous environments have a finite number of states, while discrete environments have an infinite number of states. Both discrete and continuous environments have a finite number of states. Neither discrete nor continuous environments have a finite number of states.

Discrete environments have a finite number of states, while continuous environments have an infinite number of states.

How do benign environments differ from adversarial environments in AI? Benign environments are stable and predictable, while adversarial environments are constantly changing and unpredictable. Adversarial environments are stable and predictable, while benign environments are constantly changing and unpredictable. Both benign and adversarial environments are stable and predictable. Neither benign nor adversarial environments are stable and predictable.

Benign environments are stable and predictable, while adversarial environments are constantly changing and unpredictable.

What are the different types of agents in AI? Static agents, Dynamic agents, Interactive agents, Autonomous agents Reactive agents, Proactive agents, Hybrid agents, Learning agents Deterministic agents, Nondeterministic agents, Discrete agents, Continuous agents Simple reflex agents, Model-based reflex agents, Goal-based agents, Utility-based agents

Simple reflex agents, Model-based reflex agents, Goal-based agents, Utility-based agents

What characterizes simple reflex agents in AI? They use complex algorithms to predict future states. They maintain a model of the world to make decisions. They optimize utility functions for decision-making. They respond directly to percepts with limited intelligence.

They respond directly to percepts with limited intelligence.

What is the primary function of model-based reflex agents in AI? They respond directly to percepts with limited intelligence. They act to achieve their goals. They find rules to handle the current situation. They optimize a utility function for decision-making.

They find rules to handle the current situation.

What is the main objective of goal-based agents in AI? They respond directly to percepts. They act to achieve their goals. They optimize a utility function for decision-making. They find rules to handle the current situation.

They act to achieve their goals.

Who were the pioneers in developing sophisticated programmable mechanical computers, precursors to modern electronic computers? Isaac Newton and Gottfried Leibniz Thomas Edison and Nikola Tesla Charles Babbage and Lady Lovelace Alan Turing and John von Neumann

Charles Babbage and Lady Lovelace

Which of the following are considered major types of Artificial Intelligence (AI)? Quantum computing, Blockchain, Cloud computing Expert systems, Machine learning, Neural networks Virtual reality, Augmented reality, Mixed reality Data mining, Data warehousing, Data lakes

Expert systems, Machine learning, Neural networks

What is the primary function of expert systems in AI? Store and retrieve large amounts of data efficiently Simulate human emotions and interactions Capture tacit knowledge in a specific domain and perform limited tasks Perform complex calculations faster than humans

Capture tacit knowledge in a specific domain and perform limited tasks

Machine Learning (ML) systems are designed to find patterns in data. Which of the following is an example of ML application? Spotify personalizing music playlists Adobe Photoshop editing images Google Maps providing directions Microsoft Word checking grammar

Spotify personalizing music playlists

Neural networks in AI consist of interconnected units called neurons. What is their primary function? Store and retrieve data efficiently Identify patterns and relationships in large datasets Perform basic arithmetic operations Simulate human emotions

Identify patterns and relationships in large datasets

What distinguishes deep learning neural networks from basic neural networks? They consist of multiple layers of neurons They are slower in processing data They are used for simple tasks They require less data to function

They consist of multiple layers of neurons

What is a limitation of neural networks and machine learning? They require constant human supervision They cannot answer specific questions accurately They are only applicable in gaming They are too fast for practical use

They cannot answer specific questions accurately

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