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High School
Foundations of Machine Learning
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In this course, you will deepen your understanding of machine learning. You will examine how and why the concept of machine learning was developed. Excel and Python will be used to analyze data and training models. Finally, you will discover what the future of machine learning looks like and the importance of the development cycle.
Major Topics and Concepts
Segment One
- Machine learning vs. human learning
- Abstraction
- Types of representations
- Data structures
- Search algorithms
- AI vendors
- Supervised, unsupervised, and reinforcement learning
- Learning algorithms
- Classification
- Neural networks
- Training models with data
- Personal, company, geospatial, and time-based data
Segment Two
- Problem-solving and data
- APIs, RSSs, and web scraping
- SQL and NoSQL databases
- Data wrangling
- Statistical sampling and testing
- Identifying patterns in data
- Data analysis techniques
- ML model building
- Errors in decisions and predictions
- Privacy and security concerns with data
- ML development process
- Adjust and evaluate the model
- Ethical problems related to ML
- GPUs and CPUs
- Fairness in AI
- Privacy and security concerns
Competencies
Foundations of Machine Learning
Students will demonstrate an understanding of foundations of machine learning by explaining the importance of algorithms in machine learning, describing reasoning and representation methods in machine learning, and comparing human and machine learning.
Machine Learning Algorithms
Students will demonstrate an understanding of Machine Learning Algorithms by comparing supervised and unsupervised machine learning algorithms, describing The use of a reinforcement algorithm to solve a problem, and summarizing components of neural networks.
The Role of Data in Machine Learning
Students will demonstrate an understanding of the role of data in machine learning by explaining datasets, describing the impact of data characteristics on algorithm performance, explaining the use of the Naïve Bayes Algorithm, and summarizing the bias-variance trade-off in machine learning.
Artificial Intelligence Development
Students will demonstrate an understanding of Artificial Intelligence development by describing the importance of humans in the AI development Loop, explaining goals of AI, and summarizing the role of transparency in the safe development of AI.
Data Relationships in Machine Learning
Students will demonstrate an understanding of data relationships in machine learning by explaining the relationship between data collection and data management in machine learning, explaining the process of using a data set to build a machine learning model, and summarizing the impact of GIGO on the machine learning model’s accuracy.
The Evaluation of Machine Learning Models
Students will demonstrate an understanding of the evaluation of machine learning models by explaining the use of cross-validation to measure machine learning model success, comparing evaluation strategies for machine learning models, describing the use of Isolation Forest as an evaluating model and comparing the characteristics of processors used in AI.
