Computer Science: Programming Languages & Software Engineering

Definition: Formal languages used to instruct computers to perform tasks.
Types:
- High-level Languages: Easier for humans to read (e.g., Python, Java).
- Low-level Languages: Closer to machine code (e.g., Assembly).
- Scripting Languages: Used for automating tasks (e.g., JavaScript, PHP).
Key Concepts:
- Syntax: Rules that define the combinations of symbols.
- Semantics: Meaning of the statements in the language.
- Compilation vs. Interpretation: Compiled languages (e.g., C++) are converted to machine code, while interpreted languages (e.g., Python) are executed line by line.

Definition: Application of engineering principles to software development.
Phases of Software Development Lifecycle (SDLC):
1. Requirements Analysis: Gathering user needs.
2. Design: Planning the system architecture.
3. Implementation: Writing the code.
4. Testing: Verifying the software works as intended.
5. Deployment: Releasing the software to users.
6. Maintenance: Updating and fixing software post-launch.
Models:
- Waterfall Model: Linear approach with distinct phases.
- Agile Model: Iterative approach for flexibility and collaboration.
Key Best Practices:
- Version Control: Management of changes to source code (e.g., Git).
- Code Review: Quality assurance through peer review.
- Continuous Integration/Deployment: Automating the software release process.

Definition: A subset of artificial intelligence focused on algorithms that enable computers to learn from and make predictions based on data.
Types of Learning:
- Supervised Learning: Learning from labeled data (e.g., classification, regression).
- Unsupervised Learning: Finding patterns in unlabeled data (e.g., clustering).
- Reinforcement Learning: Learning through rewards and penalties based on actions taken.
Key Concepts:
- Neural Networks: Algorithms modeled after the human brain for pattern recognition.
- Training and Testing Data: Data split into subsets to evaluate model performance.
- Overfitting vs. Underfitting: Overfitting leads to a model too tailored to the training data, while underfitting results in a model that is too simplistic.
Applications: Image recognition, natural language processing, recommendation systems.

Formal languages used to instruct computers to perform tasks
High-level Languages: Easier for humans to read and understand (e.g., Python, Java)
Low-level Languages: Closer to machine code (e.g., Assembly)
Scripting Languages: Used for automating tasks (e.g., JavaScript, PHP)
Syntax: Rules that define the combinations of symbols used in a language
Semantics: Meaning of the statements in the language
Compilation vs.Interpretation:
- Compiled languages (e.g., C++) are converted to machine code before execution
- Interpreted languages (e.g., Python) are executed line by line without a prior conversion step

The application of engineering principles to software development
Phases of Software Development Lifecycle (SDLC):
- Requirements Analysis: Gathering and documenting the user's needs
- Design: Planning the system architecture and components
- Implementation: Writing and coding the software application
- Testing: Verifying that the software functions as intended and meets requirements
- Deployment: Releasing the software to users
- Maintenance: Ongoing efforts to update, fix, and improve the software
Models:
- Waterfall Model: A linear approach with distinct phases, moving sequentially from one phase to the next
- Agile Model: An iterative approach that emphasizes flexibility and collaboration, working in short cycles
Key Best Practices:
- Version Control: Manages changes to source code (e.g., Git)
- Code Review: Quality assurance through peer review of coded projects
- Continuous Integration/Deployment: Automating the software release process for faster and more consistent deployments

A subset of artificial intelligence focused on algorithms that enable computers to learn from data and make predictions
Types of Learning:
- Supervised Learning: Uses labeled data to train models (e.g., classification, regression)
- Unsupervised Learning: Discovers patterns in unlabeled data (e.g., clustering)
- Reinforcement Learning: Learning through rewards and penalties based on actions taken
Key Concepts:
- Neural Networks: Algorithms modeled after the human brain for pattern recognition, particularly image and sound processing
- Training and Testing Data: Data is split into subsets for model training and performance evaluation
- Overfitting vs.Underfitting: Overfitting leads to a model too tailored to the training data, resulting in poor generalization to new data, while underfitting results in a model that is too simplistic and does not capture the complexity of the data
Applications:
- Image recognition: Identifying objects in images
- Natural language processing: Understanding and generating human language
- Recommendation systems: Suggesting products or content based on user preferences