Podcast
Questions and Answers
Which of the following is NOT a method to resolve a model under-fitting?
Which of the following is NOT a method to resolve a model under-fitting?
- Increase regularization. (correct)
- Increase the number of epochs or training iteration.
- Decrease regularization.
- Increase the complexity of the model.
What is the main characteristic of an over-fitting model?
What is the main characteristic of an over-fitting model?
- It performs poorly on training data but well on testing data.
- It performs poorly on both training and testing data.
- It performs well on training data but poorly on testing data. (correct)
- It performs well on both training and testing data.
In the context of machine learning, what does "regularization" refer to?
In the context of machine learning, what does "regularization" refer to?
- A technique used to prevent overfitting by adding a penalty term to the loss function. (correct)
- A technique used to increase the complexity of a model.
- A process of selecting the best features for a model.
- A method for evaluating the performance of a model on unseen data.
Which of the following scenarios would most likely indicate a model is under-fitting?
Which of the following scenarios would most likely indicate a model is under-fitting?
What is the primary reason for using a dropout layer in a neural network?
What is the primary reason for using a dropout layer in a neural network?
What is the primary advantage of machine learning over rule-based approaches in mathematical models?
What is the primary advantage of machine learning over rule-based approaches in mathematical models?
Which of the following is NOT a common Machine Learning algorithm?
Which of the following is NOT a common Machine Learning algorithm?
Which of the following is a potential disadvantage of using machine learning in decision-making?
Which of the following is a potential disadvantage of using machine learning in decision-making?
What is a potential solution to the issue of 'over-fitting' in machine learning models?
What is a potential solution to the issue of 'over-fitting' in machine learning models?
Which of the following is NOT a potential advantage of using machine learning?
Which of the following is NOT a potential advantage of using machine learning?
Which of the following visual representations is commonly used in machine learning?
Which of the following visual representations is commonly used in machine learning?
Which of the following best describes the relationship between Artificial Intelligence (AI) and Machine Learning (ML)?
Which of the following best describes the relationship between Artificial Intelligence (AI) and Machine Learning (ML)?
What is the key difference between a rule-based approach and a machine-learning approach to mathematical modeling?
What is the key difference between a rule-based approach and a machine-learning approach to mathematical modeling?
Which of the following is NOT a task commonly associated with intelligent beings, according to the content?
Which of the following is NOT a task commonly associated with intelligent beings, according to the content?
What is the primary goal of machine learning in artificial intelligence?
What is the primary goal of machine learning in artificial intelligence?
What is the key difference between the non-machine learning and machine learning approaches to solving problems?
What is the key difference between the non-machine learning and machine learning approaches to solving problems?
In the context of facial detection, what does a 'True Positive' refer to?
In the context of facial detection, what does a 'True Positive' refer to?
What is the main advantage of the machine learning approach, compared to the non-machine learning approach?
What is the main advantage of the machine learning approach, compared to the non-machine learning approach?
In the context of facial detection, what does a 'False Negative' refer to?
In the context of facial detection, what does a 'False Negative' refer to?
What is the purpose of the 'Input' and 'Output' in the diagram of the Horse algorithm?
What is the purpose of the 'Input' and 'Output' in the diagram of the Horse algorithm?
Based on the context, what is the most likely question the author is asking in the sentence 'Can a general system achieve all these tasks?'
Based on the context, what is the most likely question the author is asking in the sentence 'Can a general system achieve all these tasks?'
What is the definition of Artificial Intelligence (AI) according to the provided content?
What is the definition of Artificial Intelligence (AI) according to the provided content?
What is a classic algorithm?
What is a classic algorithm?
What is the difference between a classic algorithm and an AI algorithm?
What is the difference between a classic algorithm and an AI algorithm?
What is the role of the Programmable Computer in the AI algorithm diagram?
What is the role of the Programmable Computer in the AI algorithm diagram?
What is the goal of image classification in the context of AI?
What is the goal of image classification in the context of AI?
What is the significance of OpenCV in the context of facial recognition?
What is the significance of OpenCV in the context of facial recognition?
What are some ethical concerns related to the use of facial recognition technology?
What are some ethical concerns related to the use of facial recognition technology?
Based on the diagram provided, what are the key components of a facial recognition process?
Based on the diagram provided, what are the key components of a facial recognition process?
Flashcards
Artificial Intelligence
Artificial Intelligence
The ability of a computer to perform tasks typically requiring intelligence.
Classic Algorithm
Classic Algorithm
A step-by-step procedure for solving a problem.
Input in AI
Input in AI
The data or instructions fed into a programmable computer.
Output in AI
Output in AI
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Image Classification
Image Classification
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Programmable Computer
Programmable Computer
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Horse in AI
Horse in AI
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AI Algorithm
AI Algorithm
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Algorithm
Algorithm
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Input
Input
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Output
Output
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Machine Learning
Machine Learning
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True Positive (TP)
True Positive (TP)
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False Positive (FP)
False Positive (FP)
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True Negative (TN)
True Negative (TN)
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False Negative (FN)
False Negative (FN)
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Overfitting
Overfitting
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Underfitting
Underfitting
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Good Fitted Model
Good Fitted Model
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Epochs in Training
Epochs in Training
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Model Complexity
Model Complexity
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Machine Learning (ML)
Machine Learning (ML)
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Pros of Machine Learning
Pros of Machine Learning
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Cons of Machine Learning
Cons of Machine Learning
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AI vs. Machine Learning
AI vs. Machine Learning
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Over-fitting in ML
Over-fitting in ML
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Methods to resolve Over-fitting
Methods to resolve Over-fitting
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Common ML Algorithms
Common ML Algorithms
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Mathematical Models
Mathematical Models
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Study Notes
Introduction to Facial Recognition (EC49191)
- Course code: EC49191
- Topic: Introduction to Facial Recognition
- Chapter 1: Artificial Intelligence Definitions
Artificial Intelligence Definitions
- Artificial intelligence (AI) is a computer's or robot's ability to perform tasks usually done by intelligent beings.
What is Artificial Intelligence?
- AI is the ability of a computer or robot to do tasks that normally require human intelligence.
Artificial Intelligence
- Classic algorithm: A step-by-step procedure for solving a problem using explicit step-by-step instructions.
- Algorithm: Input goes through an algorithm to produce an output in a programmable computer
Artificial Intelligence
- Image classification: Input image (e.g., horse) is fed to an algorithm, processed by a computer, creating horse as output.
Artificial Intelligence
- Tasks associated with intelligent beings:
- Image understanding
- Natural language processing
- Knowledge acquisition
- Text understanding
- Planning
- Robotics
- Forecasting
- And many others
Machine Learning
- Definition: A branch of AI focused on computer software that learns independently.
- ML vs. GenAI comparison: ML is about software learning independently, while GenAI is broader.
Machine Learning
- Time periods marked by AI advancements:
- 1950s: Initial AI interest
- 1960s-1970s: Early Machine Learning advancements
- 1980s-1990s: Further developments in ML
- 2000s: Deep learning gains traction
- 2010s: Continued breakthroughs and disruptions
Machine Learning (in layman's terms)
- Non-machine learning: Follows predefined formulas, needs reprogramming for new conditions (e.g., wind).
- Machine learning: Identifies relationships from successes/failures, doesn't need constant reprogramming (just more data).
Facial Detection Definition
- True Positive (TP): Number of faces correctly detected.
- True Negative (TN): Number of non-faces correctly identified as non-faces.
- False Positive (FP): Number of non-faces incorrectly identified as faces.
- False Negative (FN): Number of faces incorrectly identified as non-faces.
Machine Learning
- Pros:
- Autonomous learning from data
- No need for expert human input
- Can achieve superhuman performance in specific tasks
- Cons:
- Requires substantial data
- Complex learned relationships are difficult to understand
- Can be easily misled by "bad" data
AI vs Machine Learning
- AI origins: 1950s
- Machine Learning origins: 1960s
- AI Description: Simulated intelligence in machines
- AI category: Subset within Data Science
- AI goals : Creating thinking machines
- Machine Learning Description: Training machines to learn from data
- Machine Learning category: Subset of AI and data science
- Machine Learning goals: Enabling machines to solve problems using data.
Common ML Algorithms
- Regression
- Classification
- Clustering
Over-fitting Model
- Methods to resolve overfitting:
- Reduce training iterations
- Data augmentation
- Apply regularization (e.g., dropout)
Under-fitting Model
- Methods to resolve underfitting:
- Increase training iterations
- Make model more complex
- Decrease regularization
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Description
This quiz covers the fundamentals of facial recognition, particularly focusing on the first chapter of Artificial Intelligence definitions. It explores key concepts such as AI's capabilities, algorithms, and image classification. Test your understanding of how AI can mimic human-like intelligence through various tasks.
