CSE1300 MIDTERM REVIEW Intro to Computing (History

Transcript:CSE1300 MIDTERM REVIEW Intro to Computing (History:
CSE1300 MIDTERM REVIEW Intro to Computing (History & Trends) Computers Everywhere: Phones, watches, smart TVs, cars, IoT. Historical Progress: Mainframes → affordable personal computers → smartphones & wearables. Moore’s Law: Doubles in processing power every two years (slowing down recently). Effect on Society: Automation, job shifts, global connectivity, social challenges. Example: A 1980s personal computer cost $600 with 64KB of RAM. Today, a phone might cost $600 but has 6GB RAM and multiple CPU cores—enormous growth thanks to Moore’s Law. Computer Components Case: Protects internal components, aids cooling. CPU: Central chip for processing (Intel, AMD, ARM). Runs at GHz speeds; multiple cores. RAM: Temporary working memory (4GB, 8GB, 16GB+). Motherboard: Connects all components (CPU, RAM, GPU, storage, etc.). GPU: Specialized for graphics/math-heavy tasks (3D rendering, cryptography). Storage: HDD vs. SSD vs. NVMe. Where data is kept when powered off. Example: A gaming PC has a high-end GPU to handle intense 3D computations and several fans to keep the CPU and GPU from overheating during gaming marathons. Computational Thinking Computational Thinking is a problem-solving approach involving: Decomposition – Breaking down a complex task into smaller, more manageable parts Algorithmic Thinking – Designing step-by-step instructions or procedures Abstraction – Identifying relevant information while ignoring the details Pattern Recognition – Finding similarities and repeated elements across problems Crucial for analyzing ambiguous or open-ended problems in many disciplines. Example: A student organizing a charity event applies decomposition (venue, budget, volunteers, publicity), then uses algorithmic thinking to schedule tasks in order. Decomposition Decomposition: Splitting a large task (or problem) into subtasks. Makes complex problems easier to solve, test, and maintain. Facilitates team collaboration: each person can handle a different subtask. In Computer Science, we decompose tasks (e.g., reading data, processing it, outputting results) before coding. Example: When writing a research paper, you decompose it into sections: introduction, methods, results, discussion, conclusion. Each section can be tackled independently, then combined. Algorithmic Thinking Algorithm: Precisely defined instructions for solving a problem (e.g., sorting a list, calculating area). Algorithmic Thinking: Ability to outline and analyze these steps clearly before coding or implementing. Flowcharts can visually represent algorithms (decisions, processes, inputs/outputs). Efficiency matters: We aim for solutions that are less time- or resource-intensive. Example: A bank’s ATM uses an algorithm to read your card, verify your PIN, check your balance, and dispense cash. That sequence of steps ensures a correct, secure transaction. Abstraction Abstraction: Focusing on the essential details