Artificial Intelligence : Notes
  • Supervised Learning
    • Trees
      • AdaBoost
      • ID3
      • Random Forests
    • Convolutional Neural Networks
    • DNN for Classification
    • K-Nearest Neighbors
    • LDA
    • Logistic Regression
    • Perceptron
    • QDA
    • SVM
  • Unsupervised Learning
    • DBSCAN
    • Deep Autoencoder
    • Generative Adversarial Networks (GAN)
    • K-Means Clustering
    • Linear Regression
    • Principal Component Analysis (PCA)
    • Restricted Boltzmann Machines (RBM)
  • Reinforcement Learning
    • Markov Decision Process
    • Q-Learning
    • Deep Q-Learning
  • Ensemble Strategies
    • Ensemble Learning
    • Fine-tuning and resampling
  • Other Techniques
    • Expectation-Maximization
    • Recurrent Neural Networks

Artificial Intelligence

The following is a collection of notes from the Artificial Intelligence course AI53 at UTBM. The course is taught by Fabrice Lauri and Serge Iovleff. Hopefully, the image sources are linked to the original authors. If not, please let me know.

Artificial Intelligence (AI) is a branch of computer science focused on creating systems that can perform tasks requiring human-like intelligence, including problem-solving, learning, perception, and language understanding.

Machine Learning (ML) is a subset of artificial intelligence that involves the development of algorithms and models that enable computers to learn from data, recognize patterns, and make decisions or predictions without being explicitly

Topics

  • Unsupervised Learning
  • Supervised Learning
  • Reinforcement Learning
  • Ensemble strategies