Artificial Intelligence 9
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Artificial Intelligence in Class 10 introduces students to AI concepts, such as machine learning, robotics, and natural language processing. It focuses on understanding AI’s applications in daily life, problem-solving, and ethical considerations in technology. The exam is 100 marks, with 50 marks for theory and 50 marks for practicals. The paper has four sections: 1) Employability Skills, 2) Subject-Specific Skills, 3) Practical Work, and 4) Project Work
Prerequisites
The prerequisites for learning Artificial Intelligence in Class 9 include basic computer skills, logical thinking, and an interest in technology. Students should be familiar with simple coding concepts, math basics, and have curiosity about AI and its real-life applications.
Learning Objectives
The learning objective of Artificial Intelligence is to introduce students to AI concepts, enhance problem-solving and critical thinking skills, and help them understand how AI technologies work in real-life applications, preparing them for future technological advancements.
Course Overview
1.Communication Skills-II
- Advanced verbal and written communication techniques.
- Public speaking and presentation skills.
- Effective feedback and active listening strategies.
- Cross-cultural communication and etiquette.
2: Self-Management Skills-II
- Goal setting and time management techniques.
- Stress management and resilience building.
- Personal branding and self-presentation.
- Decision-making and problem-solving skills.
3: Information and Communication Technology Skills-II
- Advanced computer operations and software applications.
- Online collaboration tools and project management.
- Cybersecurity and safe internet practices.
- Digital communication tools and platforms.
4: Entrepreneurial Skills-II
- Business planning and market analysis.
- Financial literacy and budgeting for entrepreneurs.
- Networking and relationship-building skills.
- Innovation and creativity in business.
5: Green Skills-II
- Sustainable practices in business and daily life.
- Importance of biodiversity and conservation.
- Environmental policies and regulations.
- Community involvement in environmental initiatives.
1: Introduction to Artificial Intelligence (AI)
- Definition and scope of AI
- History and evolution of AI
- Types of AI: Narrow AI vs. General AI
- Applications of AI in various fields
2: AI Project Cycle
- Understanding the AI project lifecycle
- Problem identification and goal setting
- Data collection and preparation
- Model training and evaluation
- Deployment and maintenance of AI models
3: Advanced Python
- Python libraries for AI: NumPy, Pandas, Matplotlib
- Functions and modules for data manipulation
- Advanced data structures in Python
- Error handling and debugging techniques
4: Data Science
- Introduction to Data Science
- Definition and significance of data science
- Difference between data science and AI
- Applications of Data Science
- Case studies in healthcare, finance, and marketing
- Data Science: Getting Started
- Data collection methods
- Data cleaning and preprocessing
- Data access and storage options (Practical Assessment)
5: Computer Vision
- Introduction to Computer Vision
- Applications of Computer Vision
- Computer Vision: Getting Started
6: Natural Language Processing
- Overview of Natural Language Processing (NLP)
- Applications of NLP: chatbots, sentiment analysis, language translation
- Techniques in NLP: tokenization, stemming, and lemmatization
- Understanding text classification and sentiment analysis
7: Evaluation
- Importance of model evaluation in AI
- Techniques for evaluating AI models: accuracy, precision, recall, F1 score
- Understanding confusion matrices and ROC curves
Best practices for improving model performance
- Practical Examination of Python : 15 Marks
- Practical File : 15 Marks
- Viva Voce : 05 Marks
- Project 10 Marks
- Viva Voce : 05 Marks
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Membership provided until the final examination.
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Personalized attention provided to each student.
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Certificate awarded upon completion of the course.
Monthly Tests
Regular monthly test series for progress evaluation.
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Training modules aligned with the latest CBSE syllabus.
Frequently Asked Questions
The syllabus includes AI fundamentals, data literacy, machine learning concepts, project cycle, ethics in AI, and Python programming basics.
Assessment typically consists of theory exams, practical assessments, and project evaluations, contributing to overall marks.
Basic computer skills and an interest in technology are recommended, but no prior knowledge of AI is required.
Practical work involves hands-on coding in Python, data analysis, and applying AI concepts through projects and exercises.
Yes, students are required to complete a project that applies AI concepts, encouraging practical application and understanding.
Students will develop skills in programming, critical thinking, data analysis, ethical reasoning in technology, and teamwork through projects.
