What You Need to Know Before
You Start
Starts 8 June 2025 11:42
Ends 8 June 2025
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1 hour 6 minutes
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Overview
Explore optimization techniques through turtle graphics, from hill climbing to simulated annealing, laying the groundwork for understanding neural networks and machine learning concepts.
Syllabus
- Introduction to the Course
- Turtle Graphics in Python
- Basics of Optimization
- Hill Climbing Algorithm
- Simulated Annealing
- Other Optimization Techniques
- Introduction to Neural Networks
- Practical Applications
- Project: Create an Optimized Turtle Graphic
- Course Review and Next Steps
Overview of Optimization Techniques
Importance in AI and Machine Learning
Setting up a Python Environment
Introduction to Turtle Graphics
Basic Turtle Commands and Movements
Definition and Importance
Types of Optimization Problems
Concept and Applications
Implementing Hill Climbing with Turtle Graphics
Pros and Cons of Hill Climbing
Understanding Simulated Annealing
Comparison with Hill Climbing
Implementing Simulated Annealing in Turtle Graphics
Introduction to Genetic Algorithms
Using Turtle Graphics for Visualizing Genetic Algorithms
Basic Concepts and Terminology
How Optimization Techniques are Used in Training
Case Studies and Real-world Examples
Link Between Optimization and Machine Learning
Specifying Requirements and Goals
Applying Learned Concepts
Evaluation and Presentation
Summary of Key Learnings
Pathways for Further Study in AI and Machine Learning
Subjects
Conference Talks
