High Applications of Artificial Intelligence

 

• Advancements in AI
• How AI systems are used
• Relationship between AI, humans, and the environment
• Training data
• Collecting and analyzing data
• Databases
• Relational databases
• Challenges and solutions in AI
• Software development life cycle
• Decision-making and automation in AI
• Data patterns and processing
• Supervised and unsupervised learning
• Speech transcription
• Visual object recognition
• Training machine learning (ML) models
• Embedded systems
• Career and technical student organizations

None

Besides engaging students in challenging curriculum, the course guides students to reflect on their learning and evaluate their progress through a variety of assessments. Assessments can be in the form of practice lessons, multiple choice questions, writing assignments, projects, research papers, oral assessments, and discussions. This course will use the state-approved grading scale. Each course contains a mandatory final exam or culminating project that will be weighted at 20% of the student’s overall grade.***

***Proctored exams can be requested by FLVS at any time and for any reason in an effort to ensure academic integrity. When taking the exam to assess a student’s integrity, the exam must be passed with at least a 59.5% in order to earn credit for the course. 

To achieve success, students are expected to submit work in each course weekly. Students can learn at their own pace; however, “any pace” still means that students must make progress in the course every week. To measure learning, students complete self-checks, practice lessons, multiple choice questions, projects, discussion-based assessments, and discussions. Students are expected to maintain regular contact with teachers; the minimum requirement is monthly. When teachers, students, and parents work together, students are successful.