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Map of Artificial Intelligence: Classifications, Approaches, Algorithms, Libraries, Tools, State of Art Studies, Awesome Repos, etc..

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AIM: AI Map (Map of Artificial Intelligence Developments)

Artificial intelligence is a very broad and evolving field of computer science. I am aiming to map this developing area with AI Map repository. Over time, this repo will be updated and expanded. We are waiting for your contributions.

"AI is the new electricity" Andrew Ng.

Classic AI

  • Breadth First Search Algorithm
  • Depth First Search Algorithm
    • Depth Limited Search Algorithm
  • Best First Greedy Search Algorithm
  • A* Search Algorithm
  • Local Search Algorithms
  • Min-Max Problems
  • Alpha-Beta Pruning
  • Hill Climbing Search
  • Simulated Annealing Search
  • Local Beam Search
  • Evolutionary Algorithms
    • Genetic Algorithms
    • Ant colony optimization
    • Artificial bee colony algorithm
    • Particle swarm optimization
    • Bees algorithm

Machine Learning Classification (Modern AI):

  • Supervised Learning
    • Linear Regression
    • Logistic Regression
    • Support Vector Machine
    • Decision Tree
    • K Nearest Neighbour
    • Naive Bayes Classifiers
    • Boosting
    • Ensemble Methods
    • Random Forest Trees
    • Neural Networks: "are computing systems vaguely inspired by the biological neural networks that constitute animal brains"
      • Multi Layer Perceptron
      • Deep Neural Networks
        • Deep Multi Layer Perceptron
        • Convolutional Neural Networks
          • Le-Nets
          • AlexNets
          • VGG
          • Residual Nets
          • Inception Nets
        • Recurrent Neural Networks
        • Long Short Term Memory
        • Gated Recurrent Neural Nets
        • Restricted Boltzmann Machines
        • Deep Belief Nets
        • Autoencoders
  • Unsupervised Learning
    • Clustering
      • KMeans
      • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
      • Mean-Shift Clustering
      • Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
      • Agglomerative Hierarchical Clustering
    • Self Organizing Maps
    • Transforming / Dimension Reduction
      • Principal Component Analysis(PCA)
      • Restricted Boltzmann Machines
      • Autoencoders
  • Reinforcement Learning
    • Multi-Armed Bandit Problems
      • Explore-Exploitation Dilemma
        • Epsilon-greedy
        • Optimistic Initial Values
        • UCB1
        • Thomson Sampling
    • Markov Decision Process
      • Value Function
      • Bellman Equation
      • Optimal Policy and Optimal Value Function
    • Dynamic Programming
      • Policy Iteration
      • Value Iteration
    • Monte Carlo
      • Policy Evaluation
      • Code
    • Temporal Difference Learning
      • SARSA
      • Q-Learning
    • Approximation Methods
    • Policy Gradient
    • Model-Based
    • Proximal Policy Optimization (PPO)
    • Deep Reinforcement Learning: BerkeleyDeepRLBootcamp
  • Generative Modelling
    • Generative adversarial networks (GANs)
    • Variational Auto-Encoders
    • Deep Energy Models

Machine Learning Algorithms:

  • Computer Vision:
    • Object Detection
      • Object Detection with Sliding Window
      • R-CNN
      • Fast R-CNN
      • Faster R-CNN
      • Fastest R-CNN
      • YOLO (You look only once)
    • Face Recognition:
      • One Shot Learning
      • Siamese Network
      • FaceNet
      • DeepFace
    • Neural Style Transfer: Example Implemantation
  • Sequence Models:
    • Sampling & Sequence Generation
    • Image Captioning
    • Machine Translation:
      • Attention Model
      • Bleu Score
    • Natural Language Processing:
    • Speech Recognition - Audio Data:
      • Deep Speech Recognition:
        • CTC Cost (Connectionist Temporal Classification)
      • Trigger Word Detection
  • Robotics:
    • Deep Reinforcement Learning
  • Artificial General Intelligence: MITCourse
  • Transfer Learning
  • Meta-Learning

Machine Learning Libraries and Tools:

  • Scikit Learn: ML library for Python
  • Tensorflow: An open source machine learning framework with Python
  • Keras: "The Python Deep Learning library"
  • Pytorch: is a deep learning framework with Python
  • Caffe: is a deep learning framework
  • Theano: "is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently".
  • DeepLearning4j : "Open-Source, Distributed, Deep Learning Library for the JVM, Java"
  • DarkNet: "Open Source Neural Networks in C"

Awesome Github Repos Related ML:

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