Learning Outcomes:

1. List and define essential AI terminologies such as algorithm, model, and prediction using clear metrics.

2. Illustrate the training and prediction processes with observable examples.

3. Analyze the role of key concepts through quantifiable exercises.

4. Compile a measurable glossary of technical AI terms to support further learning.

Learning Outcomes:

1. Explain the importance of data in AI with measurable demonstrations.

2. Demonstrate techniques in data cleaning and preprocessing using observable examples.

3. Identify common data quality issues with quantifiable criteria.

4. Analyze the impact of data preparation on model performance using measurable tests.

Learning Targets:

1. Identify essential ML concepts such as training, testing, and prediction.

2. Implement simple ML models using PictoBlox blocks to simulate learning processes.

3. Evaluate the performance of ML models with observable outputs and measurable accuracy.

4. Demonstrate the use of datasets in training and validating basic machine learning models.

Modules

Learning Targets:

1. Plan and outline projects specifying required AI components and interactive features.

2. Create interactive games and stories using PictoBlox’s visual block-based interface.

3. Integrate input devices such as webcams and microphones to enhance project interactivity.

4. Debug and refine project workflows to ensure reliable AI functionality.

Modules

Learning Targets:

1. Implement sensor integration techniques using webcams and microphones with PictoBlox.

2. Apply conditional logic and looping constructs to create dynamic, responsive AI projects.

3. Test AI project functionality against defined criteria and document iterative improvements.

4. Demonstrate systematic troubleshooting and optimization of block-based AI algorithms.

Modules

Learning Targets:

1. Assess the accuracy and responsiveness of AI algorithms within project settings.

2. Modify and optimize block-based codes to address identified algorithmic inefficiencies.

3. Compare project outcomes with expected benchmarks and implement necessary adjustments.

4. Document changes and improvements systematically to support ongoing project evolution.

Modules

Learning Targets:

1. Navigate the PictoBlox interface and utilize a variety of programming blocks effectively.

2. Apply advanced block-based coding techniques to build functional AI project components.

3. Integrate external hardware like webcams and microphones seamlessly with PictoBlox.

4. Leverage simulation tools within PictoBlox to demonstrate the step-by-step AI process visually.

Modules