Artificial Intelligence Course Overview

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Questions and Answers

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?

  • 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?

  • Random Forest
  • Decision Trees
  • Neural Networks
  • K-means (correct)

In reinforcement learning, what is the outcome of making an incorrect action?

<p>A feedback resulting in a penalty occurs. (D)</p> Signup and view all the answers

What defines the state (st) in reinforcement learning?

<p>The current situation perceived by the model. (A)</p> Signup and view all the answers

What is one of the main objectives of the course on Artificial Intelligence?

<p>To master unsupervised learning algorithms (A)</p> Signup and view all the answers

What percentage of the course is allocated to practical work?

<p>30% (C)</p> Signup and view all the answers

Who is the instructor for the Artificial Intelligence course?

<p>Dr. Chaymae Miloudi (B)</p> Signup and view all the answers

Which of the following is NOT part of the course plan for supervised learning?

<p>K-means clustering (D)</p> Signup and view all the answers

What are the interdisciplinary fields involved in Artificial Intelligence as mentioned?

<p>Cognitive Science and Linguistics (A)</p> Signup and view all the answers

What technical need has contributed to the emergence of AI?

<p>The explosion of data collected by humans (A)</p> Signup and view all the answers

How is the course on Artificial Intelligence evaluated according to the information provided?

<p>By a combination of presentations, assignments, and exams (B)</p> Signup and view all the answers

Which method is included in the course curriculum under unsupervised learning?

<p>Hierarchical Clustering (D)</p> Signup and view all the answers

What does a model derived from a descriptor space represent?

<p>Connections between data in a database (D)</p> Signup and view all the answers

Which step is essential for validating a model?

<p>Estimating the model's error rate (D)</p> Signup and view all the answers

In which scenario is supervised learning applied?

<p>When predicting discrete classes from labeled data (B)</p> Signup and view all the answers

What is the primary purpose of data preprocessing?

<p>To prepare data for machine learning by defining descriptor space (B)</p> Signup and view all the answers

Which method is NOT a type of supervised learning technique?

<p>K-Means Clustering (D)</p> Signup and view all the answers

What is a key characteristic of unsupervised learning?

<p>Segregates data based on similarity without predefined labels (B)</p> Signup and view all the answers

What is a common use case for unsupervised learning?

<p>Detection of anomalies in datasets (D)</p> Signup and view all the answers

Which of the following best describes semi-supervised learning?

<p>Learns from a mix of labeled and non-labeled data to improve outcomes (D)</p> Signup and view all the answers

The main objective of regression analysis in supervised learning is to:

<p>Estimate relationships between continuous variables (C)</p> Signup and view all the answers

Which transformation method is used to improve the range of values in datasets?

<p>Data normalization (A)</p> Signup and view all the answers

What does the term 'dimensionality reduction' refer to?

<p>Reducing the number of features while retaining significant information (A)</p> Signup and view all the answers

Which learning technique is described as a method that utilizes its own predictions to refine learning over time?

<p>Semi-supervised learning (B)</p> Signup and view all the answers

Which technique is primarily employed for clustering in unsupervised learning?

<p>K-Means (D)</p> Signup and view all the answers

What are the four key elements of artificial intelligence?

<p>Data, Algorithms, Performances, Scenarios (B)</p> Signup and view all the answers

Which of the following statements correctly describes deep learning?

<p>It employs deep neural networks for complex data representations. (C)</p> Signup and view all the answers

During which period did the first agent conversational (chat-bot) 'Eliza' appear?

<p>1950-1970 (D)</p> Signup and view all the answers

What is the primary focus of machine learning as a field?

<p>Developing algorithms that enable machines to learn from data. (A)</p> Signup and view all the answers

What does the 'Training Set' refer to in machine learning?

<p>A dataset used to train a machine learning model. (D)</p> Signup and view all the answers

What is the main advantage of utilizing big data in machine learning?

<p>Increased variety and volume of data for better analysis. (C)</p> Signup and view all the answers

Which of these is NOT a common method of data preprocessing?

<p>Data encryption. (B)</p> Signup and view all the answers

In what way do convolutional neural networks (CNNs) primarily benefit applications?

<p>Through enhancement of image and visual data analysis. (D)</p> Signup and view all the answers

What is the term used to describe the multiple-layered computational architecture used in deep learning?

<p>Neural network architecture. (A)</p> Signup and view all the answers

Which of the following technologies is an example of AI used for natural language processing?

<p>Voice command implementation systems. (D)</p> Signup and view all the answers

What advantage do support vector machines (SVM) offer in machine learning?

<p>They excel at high-dimensional data classification. (D)</p> Signup and view all the answers

Which of the following best describes how reinforcement learning operates?

<p>It uses rewards or penalties to learn optimal actions. (A)</p> Signup and view all the answers

What does the term 'overfitting' refer to in the context of machine learning?

<p>The model is too complex and fits noise in the training data. (B)</p> Signup and view all the answers

In machine learning, what does 'validation' typically refer to?

<p>Assessing the model's performance on a holdout dataset. (A)</p> Signup and view all the answers

Flashcards

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)?

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?

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?

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?

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?

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)?

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?

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 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

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

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 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

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

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

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

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

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

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

Replacing missing values in a dataset with reasonable estimates or removing instances with missing values.

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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

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

Converting continuous features into discrete categories, often useful for simplifying data and improving model interpretability.

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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

Selecting a subset of features from a dataset to improve model efficiency and performance.

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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

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

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

A process of building a model from a data space where rows represent samples and columns represent descriptors.

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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

A representation of data relationships in a database, used for visualization, description, classification, structuring, explanation, or prediction.

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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

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

A step in data preparation that involves removing outliers or unusual data points from the dataset.

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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

A step in data preparation that involves selecting the most informative features that contribute significantly to the learning task.

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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

A type of supervised learning where the model predicts a continuous output, such as predicting house prices or temperature.

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Clustering

A type of unsupervised learning that groups similar data points together into clusters based on their features.

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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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Study Notes

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
  • 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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