Podcast
Questions and Answers
What guides reinforcement learning?
What guides reinforcement learning?
- Static rules predetermined by the model
- User-defined objectives only
- Rewards or penalties based on errors (correct)
- Predefined algorithms without environmental interaction
Which statement about supervised learning is true?
Which statement about supervised learning is true?
- It does not require labeled samples.
- It is mainly applied for data clustering only.
- It involves using raw data for prediction. (correct)
- It is solely focused on minimizing test error.
Which learning technique is NOT part of supervised learning?
Which learning technique is NOT part of supervised learning?
- Random Forest
- Decision Trees
- Neural Networks
- K-means (correct)
In reinforcement learning, what is the outcome of making an incorrect action?
In reinforcement learning, what is the outcome of making an incorrect action?
What defines the state (st) in reinforcement learning?
What defines the state (st) in reinforcement learning?
What is one of the main objectives of the course on Artificial Intelligence?
What is one of the main objectives of the course on Artificial Intelligence?
What percentage of the course is allocated to practical work?
What percentage of the course is allocated to practical work?
Who is the instructor for the Artificial Intelligence course?
Who is the instructor for the Artificial Intelligence course?
Which of the following is NOT part of the course plan for supervised learning?
Which of the following is NOT part of the course plan for supervised learning?
What are the interdisciplinary fields involved in Artificial Intelligence as mentioned?
What are the interdisciplinary fields involved in Artificial Intelligence as mentioned?
What technical need has contributed to the emergence of AI?
What technical need has contributed to the emergence of AI?
How is the course on Artificial Intelligence evaluated according to the information provided?
How is the course on Artificial Intelligence evaluated according to the information provided?
Which method is included in the course curriculum under unsupervised learning?
Which method is included in the course curriculum under unsupervised learning?
What does a model derived from a descriptor space represent?
What does a model derived from a descriptor space represent?
Which step is essential for validating a model?
Which step is essential for validating a model?
In which scenario is supervised learning applied?
In which scenario is supervised learning applied?
What is the primary purpose of data preprocessing?
What is the primary purpose of data preprocessing?
Which method is NOT a type of supervised learning technique?
Which method is NOT a type of supervised learning technique?
What is a key characteristic of unsupervised learning?
What is a key characteristic of unsupervised learning?
What is a common use case for unsupervised learning?
What is a common use case for unsupervised learning?
Which of the following best describes semi-supervised learning?
Which of the following best describes semi-supervised learning?
The main objective of regression analysis in supervised learning is to:
The main objective of regression analysis in supervised learning is to:
Which transformation method is used to improve the range of values in datasets?
Which transformation method is used to improve the range of values in datasets?
What does the term 'dimensionality reduction' refer to?
What does the term 'dimensionality reduction' refer to?
Which learning technique is described as a method that utilizes its own predictions to refine learning over time?
Which learning technique is described as a method that utilizes its own predictions to refine learning over time?
Which technique is primarily employed for clustering in unsupervised learning?
Which technique is primarily employed for clustering in unsupervised learning?
What are the four key elements of artificial intelligence?
What are the four key elements of artificial intelligence?
Which of the following statements correctly describes deep learning?
Which of the following statements correctly describes deep learning?
During which period did the first agent conversational (chat-bot) 'Eliza' appear?
During which period did the first agent conversational (chat-bot) 'Eliza' appear?
What is the primary focus of machine learning as a field?
What is the primary focus of machine learning as a field?
What does the 'Training Set' refer to in machine learning?
What does the 'Training Set' refer to in machine learning?
What is the main advantage of utilizing big data in machine learning?
What is the main advantage of utilizing big data in machine learning?
Which of these is NOT a common method of data preprocessing?
Which of these is NOT a common method of data preprocessing?
In what way do convolutional neural networks (CNNs) primarily benefit applications?
In what way do convolutional neural networks (CNNs) primarily benefit applications?
What is the term used to describe the multiple-layered computational architecture used in deep learning?
What is the term used to describe the multiple-layered computational architecture used in deep learning?
Which of the following technologies is an example of AI used for natural language processing?
Which of the following technologies is an example of AI used for natural language processing?
What advantage do support vector machines (SVM) offer in machine learning?
What advantage do support vector machines (SVM) offer in machine learning?
Which of the following best describes how reinforcement learning operates?
Which of the following best describes how reinforcement learning operates?
What does the term 'overfitting' refer to in the context of machine learning?
What does the term 'overfitting' refer to in the context of machine learning?
In machine learning, what does 'validation' typically refer to?
In machine learning, what does 'validation' typically refer to?
Flashcards
What is Artificial Intelligence (AI)?
What is Artificial Intelligence (AI)?
A field of computer science that focuses on creating intelligent systems, often imitating human capabilities like problem-solving, learning, and decision-making.
What is Machine Learning (ML)?
What is Machine Learning (ML)?
A broad area within AI that focuses on teaching machines to learn from data, without explicit programming. It's a core concept of AI enabling machines to improve their performance over time.
What is Unsupervised Learning?
What is Unsupervised Learning?
Machine learning algorithms that infer patterns from data without labeled examples. Think clustering data into groups based on similarities. Examples include K-Means and Hierarchical Clustering.
What is Supervised Learning?
What is Supervised Learning?
Machine learning algorithms that learn from labeled data, such as the relationship between inputs and outputs. Examples include Decision Trees, K-Nearest Neighbors, and Support Vector Machines.
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What is K-Means Clustering?
What is K-Means Clustering?
A type of unsupervised learning that clusters data into K groups, where each group represents similar data points. Think grouping customers based on buying habits.
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What is a Decision Tree?
What is a Decision Tree?
A type of supervised learning that uses a tree-like structure to represent decisions and outcomes. Think predicting a loan approval based on factors like income and credit score.
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What is K-Nearest Neighbors (KNN)?
What is K-Nearest Neighbors (KNN)?
A type of supervised learning that classifies new data points based on their similarity to previously labeled data points. Think predicting a movie's genre based on its similarity to previous movies.
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What are Neural Networks & Deep Learning?
What are Neural Networks & Deep Learning?
A type of algorithm that learns using artificial neural networks, mimicking the structure of the human brain. Often used for complex tasks like image recognition, language translation, and natural language processing.
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Reinforcement Learning
Reinforcement Learning
Reinforcement learning is a type of machine learning where the AI agent learns from its interactions with an environment. It receives rewards for good actions and penalties for bad actions, improving its performance over time.
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State-Action-Reward Loop
State-Action-Reward Loop
In reinforcement learning, the AI agent is in a specific state, makes a decision, and receives a reward or penalty from the environment based on its action. This feedback loop helps the agent learn.
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Iterative Learning
Iterative Learning
This learning method is iterative, where the AI agent repeatedly interacts with the environment, updating its understanding of the world based on the received rewards and penalties.
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Supervised Learning
Supervised Learning
Supervised Learning requires labeled data, meaning each data point has a known output or target value. The algorithm learns relationships between inputs and outputs to predict new data points.
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Semi-Supervised Learning
Semi-Supervised Learning
This method uses both labeled and unlabeled data. The algorithm uses the labeled data to learn initial patterns and then uses this knowledge to predict the labels for the unlabeled data.
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Machine Learning
Machine Learning
A field of computer science focused on creating algorithms that enable machines to 'learn' from data, improving their performance in solving specific tasks without being explicitly programmed for each scenario.
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Training set
Training set
The set of data used to train a machine learning model, containing a collection of examples and their corresponding outcomes.
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Test set
Test set
The set of data used to evaluate the performance of a trained machine learning model, providing an unbiased assessment of how well the model generalizes to unseen data.
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Dataset
Dataset
A collection of data points, often structured in rows (observations) and columns (attributes or features), used as input for machine learning models.
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Data preprocessing
Data preprocessing
The process of transforming raw data into a suitable format for machine learning algorithms, often involving cleaning, normalization, and feature engineering.
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Handling missing values
Handling missing values
Replacing missing values in a dataset with reasonable estimates or removing instances with missing values.
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Normalization
Normalization
Transforming features in a dataset to have a specific range or distribution, often useful for improving model performance.
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Outlier detection
Outlier detection
Identifying and removing instances in a dataset that differ significantly from other data points, often caused by errors or anomalies.
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Discretization
Discretization
Converting continuous features into discrete categories, often useful for simplifying data and improving model interpretability.
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Feature engineering
Feature engineering
The process of extracting useful features from raw data, often involving transforming or combining existing features to improve model performance.
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Feature selection
Feature selection
Selecting a subset of features from a dataset to improve model efficiency and performance.
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Training
Training
The process of training a machine learning model by feeding it with data and allowing it to learn patterns and relationships from the data.
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Testing
Testing
The process of evaluating the performance of a trained machine learning model on unseen data, providing insights into its generalization ability.
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Deployment
Deployment
The process of making a trained machine learning model available for use in a real-world application, often involving deploying the model to a server or integrating it into existing systems.
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Machine Learning Model Construction
Machine Learning Model Construction
A process of building a model from a data space where rows represent samples and columns represent descriptors.
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Model Validation
Model Validation
The process of evaluating a model's performance and accuracy before using it in real-world scenarios. It includes steps like calculating error rates, improving the model based on results, and seeking expert validation.
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Machine Learning Model
Machine Learning Model
A representation of data relationships in a database, used for visualization, description, classification, structuring, explanation, or prediction.
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Data Preparation
Data Preparation
The process of preparing data for machine learning by cleaning, transforming, and selecting relevant features.
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Data Transformation: Categorical Conversion
Data Transformation: Categorical Conversion
A step in data preparation that involves converting continuous attributes into categorical ones, such as changing age to age groups.
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Data Transformation: Outlier Removal
Data Transformation: Outlier Removal
A step in data preparation that involves removing outliers or unusual data points from the dataset.
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Data Transformation: Normalization
Data Transformation: Normalization
A step in data preparation that involves scaling data values to a common range, ensuring that all attributes have a similar scale.
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Data Transformation: Feature Selection
Data Transformation: Feature Selection
A step in data preparation that involves selecting the most informative features that contribute significantly to the learning task.
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Classification
Classification
A type of supervised learning where the model predicts a categorical output, such as classifying emails as spam or not spam.
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Regression
Regression
A type of supervised learning where the model predicts a continuous output, such as predicting house prices or temperature.
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Clustering
Clustering
A type of unsupervised learning that groups similar data points together into clusters based on their features.
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Dimensionality Reduction
Dimensionality Reduction
A type of unsupervised learning that reduces the dimensionality of data by finding principal components, which capture the most variance in the data.
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Course Objectives
- Introduce the field of Artificial Intelligence (AI)
- Present core Machine Learning (ML) concepts
- Master unsupervised and supervised learning algorithms
- Master necessary Python APIs for data processing, analysis, and visualization
Course Structure
- Theoretical component: 50%
- Tutorials: 20%
- Practical work: 30%
- Evaluation:
- Formula 1: 25%
- Presentation: 25%
- Practical Exercises (TP): 50%
- Formula 2: 45%
- Midterm: 55%
Course Outline (Theoretical)
- Introduction to AI
- Introduction to Machine Learning
- Unsupervised Learning
- Hierarchical Ascending Classification
- K-Means Clustering
- Supervised Learning
- K-Nearest Neighbors
- Decision Trees
- Unsupervised Learning
- Neural Networks & Deep Learning
Course Outline (Practical)
- Scientific Computing
- Data Exploration
- Graphical Visualization
- Unsupervised Learning
- Supervised Learning
Introduction to Artificial Intelligence (AI)
- AI is the field of computer science focused on creating intelligent machines.
- Proposed in 1956 by John McCarthy.
- AI draws on philosophy, cognitive sciences, logic, psychology, linguistics, etc.
Why AI?
- Explosion of data
- Advancements in data processing algorithms
- Exponential increase in computing power
Approaches to AI
- Symbolic AI: Represents data with symbols, using logic and mathematics
- Connectionist AI: Represents data as numbers, vectors, or matrices emphasizing simulations of the human brain
- Actionist AI: Focuses on interaction with the environment
AI, Machine Learning (ML) & Deep Learning
- AI encompasses these concepts but has a broader scope (including human-like intelligence).
- ML is a subset of AI.
- Deep Learning is a subset of ML.
History of AI
- Early (1950-1970): Foundation laid (Turing Test, emergence of the term AI), early neural networks (Perceptron), and logical reasoning.
- Mid (1980-1990): Progress in neural networks, expert systems, Bayesian networks, self-organizing maps.
- Modern (2010-present): Rise of Big Data, emphasis on Machine Learning with vast datasets, and emergence of Deep Learning (neural networks with many layers).
AI Application Areas
- Image recognition
- Speech processing
- Natural language processing
Specific AI Applications
- Facial recognition, chatbot interaction, voice navigation
- Medical diagnostics (cancer detection), translation
- Education, lie detection and sentiment analysis
Machine Learning Fundamentals
- Machine learning creates algorithms that computers use to learn from data to solve problems.
- Input (data), task, and measured performance are crucial.
- Examples include housing price prediction, or client grouping.
Machine Learning Algorithm vs Rules
- Machine learning algorithms automatically derive rules from data.
- Rule-based systems use predefined rules.
Dataset Components
- Dataset: raw data used for training
- Training set: data to train the model
- Test set: data to assess the test performance.
Dataset Structure
- Attributes/Features: Characteristics describing the data
- Classes: target values or labels.
Machine Learning Phases
- Data Preparation
- Training
- Validation
- Deployment
- Integration & Feedback
Data Preprocessing Techniques
- Missing value imputation
- Outlier detection
- Feature engineering (creating new features)
- Feature scaling (normalizing data)
Model Validation
- Essential to evaluate a model's accuracy and refine it accordingly.
Machine Learning Types
- Supervised Learning: learns from labeled data to predict classes or values.
- Eg: Classification, Regression
- Unsupervised Learning: discovers patterns in unlabeled data.
- Eg: Clustering, dimensionality reduction, outlier detection
- Semi-supervised learning: Leverages labeled and unlabeled data.
- Eg: Techniques like self-training to improve performance
- Reinforcement Learning: learns through trial and error, receiving rewards or penalties for actions.
Supervised Learning Techniques
- Classification
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Trees
- Random Forests
- Gradient Boosting
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Regression
Unsupervised Learning Techniques
- Clustering:
- Hierarchical Clustering
- K-Means
- Dimensionality Reduction:
- Principal Component Analysis (PCA)
- Isomap
- Outlier Detection:
- One-class SVM
- Isolation Forest
Reinforcement Learning
- Model interacts with an environment, making decisions based on rewards or penalties.
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