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Questions and Answers
How does machine learning primarily acquire knowledge?
How does machine learning primarily acquire knowledge?
- Through random chance
- By learning from data (correct)
- By following pre-programmed instructions
- Through explicit user commands
What is a key difference between traditional programming and machine learning?
What is a key difference between traditional programming and machine learning?
- Traditional programming requires explicit rules to process data, while machine learning learns rules from data. (correct)
- Traditional programming uses data to generate rules, while machine learning uses rules to process data.
- Traditional programming is suitable for complex problems, while machine learning is best for simple tasks.
- Traditional programming handles only numerical data, while machine learning works with categorical data.
Consider a dataset with 'x' and 'y' values. Given the following pairs: (1, 3), (2, 5), (3, 7), (4, 9), what 'y' value would machine learning predict for x=5, assuming a linear relationship?
Consider a dataset with 'x' and 'y' values. Given the following pairs: (1, 3), (2, 5), (3, 7), (4, 9), what 'y' value would machine learning predict for x=5, assuming a linear relationship?
- 11 (correct)
- 10
- 12
- 8
Given a dataset with some 'noise' in the 'y' values: (1, 7.2), (2, 9.1), (3, 10.9), (4, 13.3), which function best models the relationship to predict 'y' for a new 'x' value?
Given a dataset with some 'noise' in the 'y' values: (1, 7.2), (2, 9.1), (3, 10.9), (4, 13.3), which function best models the relationship to predict 'y' for a new 'x' value?
Machine learning models can handle complex relationships, with higher dimensions represented as what?
Machine learning models can handle complex relationships, with higher dimensions represented as what?
In a dataset, if x1 represents one variable and x2 represents another, and the result is determined by the rule: Result = 1 if x1 > 50 and x2 > 50, otherwise Result = 0. What would the result be for x1 = 55 and x2 = 51?
In a dataset, if x1 represents one variable and x2 represents another, and the result is determined by the rule: Result = 1 if x1 > 50 and x2 > 50, otherwise Result = 0. What would the result be for x1 = 55 and x2 = 51?
Which type of predictive task involves assigning data points to predefined categories?
Which type of predictive task involves assigning data points to predefined categories?
Which machine learning task is best suited for predicting continuous values, such as house prices?
Which machine learning task is best suited for predicting continuous values, such as house prices?
Predicting whether a student will be placed based on their CGPA, IQ, and written test scores is an example of what?
Predicting whether a student will be placed based on their CGPA, IQ, and written test scores is an example of what?
Identifying whether an email is spam or not is best addressed through which type of machine learning task?
Identifying whether an email is spam or not is best addressed through which type of machine learning task?
Which of the following is a key characteristic of machine learning?
Which of the following is a key characteristic of machine learning?
What characterizes supervised learning in machine learning?
What characterizes supervised learning in machine learning?
Which type of machine learning involves an agent learning to make decisions in an environment to maximize a reward?
Which type of machine learning involves an agent learning to make decisions in an environment to maximize a reward?
Which machine learning type is characterized by finding patterns and relationships in data without explicit labels?
Which machine learning type is characterized by finding patterns and relationships in data without explicit labels?
In the machine learning workflow, what immediately follows the data collection phase?
In the machine learning workflow, what immediately follows the data collection phase?
Which of these options represents a major challenge in traditional machine learning?
Which of these options represents a major challenge in traditional machine learning?
Deep learning is particularly effective when dealing with what type of data and problems?
Deep learning is particularly effective when dealing with what type of data and problems?
Which of the following best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
Which of the following best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
In the context of neural networks, what is the role of a neuron?
In the context of neural networks, what is the role of a neuron?
What does 'forward propagation' refer to in the context of neural networks?
What does 'forward propagation' refer to in the context of neural networks?
If a neuron has inputs $x_1$, $x_2$, and $x_3$ with corresponding weights $w_1$, $w_2$, and $w_3$, what is the output before applying the activation function, according to the diagram?
If a neuron has inputs $x_1$, $x_2$, and $x_3$ with corresponding weights $w_1$, $w_2$, and $w_3$, what is the output before applying the activation function, according to the diagram?
In an artificial neuron, what role does the 'bias' (b) typically play?
In an artificial neuron, what role does the 'bias' (b) typically play?
What is the purpose of the 'step function' in a simple perceptron model?
What is the purpose of the 'step function' in a simple perceptron model?
Given the equation for a perceptron's output: Output = 0 if w^T * X + b < threshold and Output = 1 if w^T * X + b > threshold, what does $w^T * X$ represent?
Given the equation for a perceptron's output: Output = 0 if w^T * X + b < threshold and Output = 1 if w^T * X + b > threshold, what does $w^T * X$ represent?
What distinguishes deep learning from traditional machine learning?
What distinguishes deep learning from traditional machine learning?
Flashcards
Artificial Intelligence
Artificial Intelligence
Designing intelligent agents to receive percepts and take actions in an environment.
Machine Learning
Machine Learning
A subset of AI where computers learn from data without explicit programming.
Machine Learning Model
Machine Learning Model
ML finds patterns in data to create a model to estimate new values.
Supervised Learning
Supervised Learning
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Unsupervised Learning
Unsupervised Learning
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Reinforcement Learning
Reinforcement Learning
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Classification
Classification
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Regression
Regression
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Feature
Feature
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Deep Learning Motivation
Deep Learning Motivation
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Deep Learning
Deep Learning
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Artificial Neuron
Artificial Neuron
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Weighted Inputs
Weighted Inputs
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Weighted Sum
Weighted Sum
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Step Function
Step Function
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Study Notes
- Project School: Embedded Learning, 2024-2025
Introduction
- Deep Learning, Topic: Introduction To Neural Networks
Chapter Breakdown
- Chapter 1: Understand Machine Learning in brief, Applications of Machine Learning
- Chapter 2: Types of Machine Learning, ML Algorithms for reference
- Chapter 3: Neural Networks: A game changer, Perceptron, Forward Propagation
Artificial Intelligence (AI)
- Involves designing and building intelligent agents.
- These agents receive percepts from the environment.
- These agents take actions that affect their environment.
- Mimicking the intelligence or behavioral pattern of humans or any other living entity.
AI Techniques/Algorithms
- Evolutionary algorithms
- Expert systems
- Search algorithms (e.g., A*)
- Machine learning
Machine Learning Basics
- Machine Learning can learn from data.
- Data helps to learn from experience.
- Machine follows instructions
- Traditional programming inputs Data and Set of Rules to the computer which give results
- Machine Learning puts Data and Results into the computer which in turn figures out the Set of Rules, or Model
Example: Predicting Y Values
- Given x and y values, the task is to predict the value of y for a new x value.
- For x = 1, y = 3; x = 2, y = 5; x = 3, y = 7; x = 4, y = 9; therefore, for x = 5, y = 11.
- The predictive model is Y = 2x + 1.
- When given a noisy data set such as x = 1, y = 7.2; x = 2, y = 9.1; x = 3, y = 10.9; x = 4, y = 13.3; x = 5 is 15 using the equation f(x) = 2x + 5
- The correct equation would be k(x) = 2.1x + 7.2
Geometric Representation
- The equation Z = 2x1 + 3x2 + 1 represents a plane.
- In higher dimensions, it becomes a hyperplane.
Exercise: Predicting Results Based on Two Variables
- Given x1 and x2, predict the Result.
- If x1 = 55, x2 = 65, Result = 1; x1 = 45, x2 = 60, Result = 0; x1 = 52, x2 = 55, Result = 1; x1 = 48, x2 = 49, Result = 0; x1 = 60, x2 = 70, Result = 1; hence, x1 = 55, x2 = 51, Result = 1.
- Rule: Result = 1 if x1 > 50 and x2 > 50; Result = 0 if x1 <= 50 or x2 <= 50.
Traditional Programming Example
- In traditional programming, you need to explicitly define the conditions.
- Example:
- x1 = int(input("Enter a number for x1: "))
- x2 = int(input("Enter a number for x2: "))
- if x1 <= 50: print ("0")
- elif x2 <= 50: print ("0")
- else: print ("1")
Types of Predictive Tasks
- Classification: splits discrete or categorical data and has a category associated, such as tumor classification.
- Regression: fits real number data and has an associated number, such as prediction of stock market.
Feature Examples
- Housing Price Prediction considers area, rooms, and parking.
- Placement Prediction considers cgpa, IQ, and written test.
Machine Learning Tasks
- Predicting house prices based on location and size
- Identifying spam emails in an inbox
- Diagnosing disease based on medical test results
- Estimating stock market prices over time
- Forecasting daily temperature from historical weather data
- Identifying animals from images (e.g., cats vs. dogs)
Machine Learning in Brief
- Computer performs a task without using explicit instructions
- Computer learns from data
Machine Learning Types
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
Reinforcement Learning
- Examples are Chess, Shogi, and Go
ML Workflow
- Receive data
- Analyze data
- Find patterns
- Make predictions
- Send answer
Traditional Machine Learning - Challenges
- Feature Engineering - Manual/domain-expert
- (Relatively) small dataset
- Less computation intensive
- Ideal for "simple” problems
AI, Machine Learning, and Deep Learning Relationships
- AI mimics the intelligence or behavioral patterns of living entities.
- Machine Learning is a technique for computers to "learn" from data without complex rules, based on training a model from datasets examples: Support Vector Machine and Random Forest
- Deep Learning is a technique to perform machine learning inspired by brain's neurons example: Convolutional Neural Network
Deep Learning - Motivation
- Requires a very large dataset
- Suited for Complex problems where traditional ML fails
- Needs access to extensive computational resources
Neuron Structure and Function
- Neuron receives inputs (x1, x2, x3) with associated weights (w1, w2, w3).
- Output = w1 * x1 + w2 * x2 + w3 * x3
- Artificial Neuron:
- Summer takes the sum of x(i) * w(i) + b
- Threshold unit decides the ouput
Perceptron
- z = w1* X1 + w2* X2 + w3* X3 + w4X4... + wnxn + b
- Output = 0 if ∑wjxj + b < threshold
- Output = 1 if ∑wjxj + b > threshold, where w is weight and b is bias.
- Output = 0 if w(transpose)X + b < threshold
- Output = 1 if w(transpose)X + b > threshold
- In this equation X are inputs and w is the assigned weight
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