Table of Contents
Event Overview
The AI Olympiad is a competitive event designed for Grade 9-12 students and gap-year learners passionate about artificial intelligence and machine learning. This competition will assess participants on their knowledge of Python programming, machine learning concepts, data preprocessing, and problem-solving skills.
Eligibility Criteria
- Open to students in Grades 9-12 or gap-year students (not enrolled in undergraduate programs).
- Participants should have a basic understanding of Python and introductory concepts in AI/ML.
Events Format and Timeline
Time | Event |
11: 00 AM | Program commences |
11: 00 AM -12: 00 PM | Round 1: MCQs |
12: 00 PM – 12:30 PM | Round 2: Presentation session |
12: 30 PM – 1:30 PM | Break (snacks, refreshments) |
1:30 PM – 3:00 PM | Round 2: Subjective Questions |
3:00 PM – 4:00 PM | Interactive Physical Game |
4:00 PM – 4:30 PM | Winner Announcement and Certifications |
Round Structure
Round 1: Multiple-Choice Questions (50%)
- 30 MCQs with 4 options each, single correct answer.
- Focus: Testing foundational knowledge and conceptual understanding.
Round 2: Subjective Questions & Problem Solving (50%)
- Tasks:
- Find the output of given Python code snippets.
- Complete code to solve specific AI/ML problems.
- Answer reasoning-based questions to assess problem-solving skills.
Rules and Regulations
- Only students from Grades 9-12 or gap-year students (not enrolled in undergraduate programs) are eligible.
- Cheating or plagiarism will result in immediate disqualification.
- Entry fees are non-refundable.
- Collaboration is not allowed; all submissions must reflect individual effort.
Syllabus and Resources
- Python Basics: Syntax, data types, loops, conditionals.
- Introduction to Machine Learning: Supervised, unsupervised, and reinforcement learning.
- Common ML Algorithms: Linear Regression, Logistic Regression, Decision Trees, KNN, SVM.
- Data Preprocessing Techniques: Handling missing values, normalization, encoding.
- Evaluation Metrics: Accuracy, Precision, Recall, F1-score.
- Machine Learning Libraries: NumPy, Pandas, Matplotlib, Scikit-Learn.
- Deep Learning Fundamentals: Neural networks, activation functions, feedforward, backpropagation.
Practice Questions & Study Material:
Learn ML from ML crash course
Access sample questions and preparation resources here Resources
