Modules

Artificial Intelligence and Machine Learning Notes

Introduction to Artificial Intelligence

• Definition of AI
• History and evolution of AI
• Types of AI: Narrow vs. General AI
• Applications of AI in various fields (healthcare, finance, etc.)

Machine Learning Basics

• Definition of Machine Learning
• Differences between AI, ML, and Deep Learning
• Types of Machine Learning:
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

• Definition and examples
• Common algorithms:
  • Linear Regression
  • Decision Trees
  • Support Vector Machines
  • Neural Networks
• Evaluation metrics (accuracy, precision, recall, F1-score)

Unsupervised Learning

• Definition and examples
• Common algorithms:
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
• Applications (market segmentation, anomaly detection)

Reinforcement Learning

• Definition and concepts (agent, environment, rewards)
• Key algorithms:
  • Q-Learning
  • Deep Q-Networks (DQN)
• Applications (game playing, robotics)

Neural Networks and Deep Learning

• Introduction to neural networks
• Structure of a neuron and layers (input, hidden, output)
• Activation functions (ReLU, Sigmoid, Softmax)
• Backpropagation and training process
• Common architectures (Convolutional Neural Networks, Recurrent Neural Networks)

Natural Language Processing (NLP)

• Overview of NLP
• Text preprocessing techniques (tokenization, stemming, lemmatization)
• Common algorithms (Bag of Words, TF-IDF)
• Applications (chatbots, sentiment analysis, machine translation)

Model Evaluation and Selection

• Train-test split and cross-validation
• Overfitting vs. underfitting
• Hyperparameter tuning
• Importance of feature selection and engineering

Tools and Libraries

• Overview of popular libraries (TensorFlow, PyTorch, Scikit-Learn)
• Introduction to Jupyter Notebooks for experimentation
• Data manipulation libraries (Pandas, NumPy)

Ethics in AI

• Importance of ethical considerations in AI
• Bias in AI models
• Privacy and data security concerns