Artificial Intelligence (AI) Fundamentals

Study Notes

AI is a subfield of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence.
Key areas in AI:
- Machine Learning (ML): developing algorithms that enable machines to learn from data.
- Natural Language Processing (NLP): enabling machines to understand and generate human language.
- Computer Vision: enabling machines to interpret and understand visual data.
Applications of AI:
- Expert systems
- Robotics
- Image and speech recognition

Database Management Systems (DBMS) are designed to store, manage, and retrieve data efficiently.
Key concepts:
- Data modeling: conceptual representation of data structures.
- Data normalization: organizing data to minimize data redundancy.
- Query languages: SQL, querying, and data retrieval.
Types of databases:
- Relational databases (e.g., MySQL)
- NoSQL databases (e.g., MongoDB)

Algorithm design involves developing step-by-step procedures to solve a problem.
Key concepts:
- Algorithm complexity: time and space complexity analysis.
- Big-O notation: measuring the upper bound of an algorithm's complexity.
- Algorithm design strategies:
  - Brute force
  - Divide and conquer
  - Dynamic programming
Algorithm design techniques:
- Recursion
- Memoization

AI is a subfield of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence.
Key areas in AI include:
- Machine Learning (ML): developing algorithms that enable machines to learn from data.
- Natural Language Processing (NLP): enabling machines to understand and generate human language.
- Computer Vision: enabling machines to interpret and understand visual data.
Applications of AI include:
- Expert systems
- Robotics
- Image and speech recognition

Database Management Systems (DBMS) are designed to store, manage, and retrieve data efficiently.
Key concepts include:
- Data modeling: conceptual representation of data structures.
- Data normalization: organizing data to minimize data redundancy.
- Query languages: SQL, querying, and data retrieval.
Types of databases include:
- Relational databases (e.g., MySQL)
- NoSQL databases (e.g., MongoDB)

Data structures are ways to organize and store data in a computer.
Key data structures include:
- Arrays
- Linked lists
- Stacks
- Queues
- Trees
- Graphs
Operations on data structures include:
- Insertion
- Deletion
- Traversal
- Searching

Algorithm design involves developing step-by-step procedures to solve a problem.
Key concepts include:
- Algorithm complexity: time and space complexity analysis.
- Big-O notation: measuring the upper bound of an algorithm's complexity.
- Algorithm design strategies include:
  - Brute force
  - Divide and conquer
  - Dynamic programming
Algorithm design techniques include:
- Recursion
- Memoization

Computer systems encompass the hardware and software components of a computer.
Key concepts include:
- Hardware components: CPU, Memory, Input/Output devices
- Software components: Operating Systems, Compilers, Assemblers
System architecture includes:
- Von Neumann architecture
- Harvard architecture
System performance metrics include:
- Speed
- Throughput
- Response time