Data Structures Overview

Definition: Organized formats to store and manage data efficiently for computer algorithms.
Types:
- Primitive Data Structures: Basic types like integers, floats, characters, and booleans.
- Non-Primitive Data Structures:
  - Linear:
    - Arrays: Fixed-size, indexed collections of elements.
    - Linked Lists: Nodes containing data and pointers to the next node.
    - Stacks: LIFO (Last In, First Out) structure; push and pop operations.
    - Queues: FIFO (First In, First Out) structure; enqueues and dequeues.
  - Non-Linear:
    - Trees: Hierarchical structures with nodes, e.g., binary trees, AVL trees.
    - Graphs: Sets of vertices connected by edges; can be directed or undirected.
Operations:
- Insertion, deletion, traversal, searching, and sorting.
Complexity Considerations:
- Time Complexity: Measure of time taken to execute an operation (e.g., Big O notation).
- Space Complexity: Measure of memory used by data structures.

Definition: A subset of artificial intelligence involving algorithms that allow computers to learn from data.
Types:
- Supervised Learning: Models are trained on labeled data (input-output pairs).
  - Algorithms: Linear regression, logistic regression, decision trees, support vector machines.
- Unsupervised Learning: Models identify patterns in unlabeled data.
  - Algorithms: K-means clustering, hierarchical clustering, principal component analysis (PCA).
- Reinforcement Learning: Agents learn by interacting with an environment to maximize cumulative reward.
  - Techniques: Q-learning, deep reinforcement learning.
Key Concepts:
- Training Set: Subset of data used to train the model.
- Test Set: Subset of data used to evaluate model performance.
- Overfitting: When a model learns noise in the training data rather than the underlying pattern.
- Underfitting: When a model is too simple to capture the data's structure.
Applications: Image recognition, natural language processing, recommendation systems, autonomous systems.

Organized formats designed for efficient data storage and management, crucial for algorithm performance.
Primitive Data Structures: Fundamental types including:
- Integers, floats, characters, and booleans serve as building blocks for more complex structures.
Non-Primitive Data Structures: Divided into linear and non-linear categories.
- Linear:
  - Arrays: Collections with fixed size and indexed access.
  - Linked Lists: Composed of nodes, each containing data and a reference to the next node, allowing dynamic size.
  - Stacks: Last In, First Out (LIFO) structure, allowing operations like push (add) and pop (remove).
  - Queues: First In, First Out (FIFO) structure, enabling operations such as enqueue (add) and dequeue (remove).
- Non-Linear:
  - Trees: Hierarchical data structures featuring nodes; include binary trees and balanced AVL trees for efficient searching.
  - Graphs: Consist of vertices (nodes) connected by edges, can be directed (one-way connections) or undirected (two-way connections).
Operations: Critical operations include insertion, deletion, traversal (visiting nodes), searching for specific values, and sorting data.
Complexity Considerations:
- Time Complexity: Indicates how the execution time grows relative to the size of the input, often expressed in Big O notation (e.g., O(n), O(log n)).
- Space Complexity: Reflects the amount of memory required by a data structure in relation to the input size.

Subset of artificial intelligence that empowers systems to learn patterns and make decisions based on data.
Types:
- Supervised Learning: Models learn from labeled datasets, where input-output pairs guide the training.
  - Common algorithms include linear regression, logistic regression, decision trees, and support vector machines.
- Unsupervised Learning: Focuses on discovering underlying patterns in unlabeled data.
  - Techniques include K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Reinforcement Learning: Involves agents learning optimal behaviors through interactions with their environment to maximize rewards.
  - Utilizes methods such as Q-learning and deep reinforcement learning for strategic decision-making.
Key Concepts:
- Training Set: The portion of data used to fit models and tune parameters.
- Test Set: A distinct dataset used to evaluate the performance and generalization of the model.
- Overfitting: Occurs when a model captures noise rather than true patterns, leading to poor performance on unseen data.
- Underfitting: Happens when a model is overly simplistic and fails to capture the data’s underlying structure.
Applications: Widely leveraged in fields such as image recognition, natural language processing, recommendation systems, and autonomous vehicles.