PANTOMATH

Posted: **Thu Dec 26, 2024 11:32 am**

Artificial Intelligence and Machine Learning

1. Artificial Intelligence (AI)

Definition:
- AI involves building systems that can mimic human cognition, such as reasoning, problem-solving, and decision-making.
Subfields of AI:
- Expert Systems: AI systems that emulate decision-making by using knowledge bases (e.g., medical diagnosis systems).
- Search Algorithms:
  - Uninformed Search: Breadth-First Search (BFS), Depth-First Search (DFS).
  - Informed Search: A* Algorithm, Heuristic Search.
- Game Playing: AI algorithms for chess, Go, and other strategy games.

2. Machine Learning (ML)

Definition:
- A subset of AI where machines learn from data without being explicitly programmed.
Types of Machine Learning:
1. Supervised Learning:
  - Models learn from labeled data (e.g., classification, regression).
  - Algorithms: Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees.
2. Unsupervised Learning:
  - Models identify patterns in unlabeled data (e.g., clustering, dimensionality reduction).
  - Algorithms: K-Means, DBSCAN, PCA (Principal Component Analysis).
3. Reinforcement Learning:
  - Agents learn by interacting with an environment and receiving feedback (rewards).
  - Examples: Q-Learning, Deep Q-Networks (DQN).

3. Deep Learning

Overview:
- A subset of ML focusing on neural networks with many layers.
Key Concepts:
- Neural Networks: Input, hidden, and output layers.
- Activation Functions: ReLU, Sigmoid, Softmax.
- Backpropagation: Training neural networks by minimizing error.
Applications:
- Image Recognition, Natural Language Processing (NLP), Speech Recognition.

4. Natural Language Processing (NLP)

Overview:
- Focuses on enabling machines to understand, interpret, and generate human language.
Key Techniques:
- Tokenization, Stopword Removal, Stemming, Lemmatization.
- Sentiment Analysis, Machine Translation, Text Summarization.
Advanced Models:
- Transformers (e.g., BERT, GPT), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs).

5. Computer Vision

Overview:
- Enables machines to interpret visual data like images and videos.
Techniques:
- Image Processing: Filtering, Edge Detection.
- Object Detection: YOLO, SSD, Faster R-CNN.
- Facial Recognition and Optical Character Recognition (OCR).
Applications:
- Autonomous Vehicles, Medical Imaging, Augmented Reality.

6. Tools and Frameworks

Programming Languages: Python, R, Julia.
Libraries and Frameworks:
- TensorFlow, PyTorch, Scikit-Learn, Keras, OpenCV.
Cloud AI Services:
- Google AI, AWS Machine Learning, Microsoft Azure AI.