This week’s lessons strengthened my understanding of how algorithm efficiency is measured using Big-O, Big-Theta, and Big-Omega notations. The lecture notes emphasized identifying the basic operation and using the dominant term to classify an algorithm’s growth order. Through quizzes, I learned why Big-Theta can only be used when an algorithm has the same time complexity in all cases, while Big-O represents an upper bound. The homework project applied these ideas in practice by showing how sorting often dominates overall runtime, leading to (n log n) complexity. Analyzing recursive algorithms using recurrence relations and backward substitution also helped clarify how time complexity evolves across recursive calls.
Part 1: Review and Reflect Learning Strategy One of my strengths is making and revising a study schedule. I am good at planning my time and organizing my tasks, which helps me stay on track. I also do well at finding the main idea and important details when I read. This skill helps me understand the most important parts of the material. However, I need to improve in a few areas. Taking notes is hard for me because sometimes I write too much or miss key points. I also struggle with outlining textbooks since I am not always sure what to include. Another challenge for me is answering multiple-choice questions. Sometimes I overthink the answers and doubt myself. Part 2: Preview Time Management Skills Link: https://docs.google.com/spreadsheets/d/1KNpOCEeEtB0whGXmduCl-BP1txBSaqMnxBG3fvfV-AE/edit?usp=sharing Part 3: Project Management Basics I watched three videos about project management. They helped me understand important concepts. The first video was about the basics of project man...
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