Innov-AI-Thon

Innov-AI-thon is a structured, beginner-focused competition designed to introduce first, second, and third-year students to the fundamentals of Artificial Intelligence and Machine Learning. Unlike typical hackathons that focus solely on final output, this challenge emphasizes the learning process, conceptual clarity, and the ability to explain technical decisions.The event follows a progressive three-round structure, moving from theoretical knowledge to hands-on implementation, culminating in a defense interview. The core objective is to build strong foundational skills and encourage hands-on experimentation with Python and ML workflows. Focus Domains: Participants will gain exposure to and be tested on: Artificial Intelligence Fundamentals Machine Learning Basics Python for ML Data Handling and Preprocessing Model Training and Evaluation Explanation and Reasoning of ML Workflows Eligibility and Logistics: Eligibility: Open to 1st, 2nd, and 3rd Year Undergraduate Students. Team Size: 2-3 participants per team. Mode: Hybrid (Round 1 Online | Rounds 2 & 3 Offline). Requirements: Participants must bring a personal laptop with basic programming tools installed. Competition Structure: Round 1: Online Quiz (Screening Round): Mode: Online Format: MCQ-based Content: Questions covering AI/ML fundamentals, basic Python, introductory statistics, and general AI awareness. Selection: Teams will be shortlisted for the offline round based on their scores. Round 2: Model Training Challenge (Hands-On): Mode: Offline (On-Campus) Task: Teams will be provided with a predefined dataset and a problem statement. Deliverables: Participants must preprocess the data, select a model, train it, and evaluate its performance. Goal: To assess practical execution and dataset handling skills. Round 3: Interview & Interaction Round: Mode: Offline Format: A Viva/Interview session with the judging panel. Focus: Teams must explain their model selection rationale, training workflow, and challenges faced. This round tests conceptual clarity over code complexity. Rewards and Prizes: To encourage participation across all levels, prizes are awarded year-wise: 1st Year Winner: ₹3,000 + Certificate 2nd Year Winner: ₹3,000 + Certificate 3rd Year Winner: ₹3,000 + Certificate Special Recognition Awards: Best Overall Performance Best Conceptual Understanding Best Model Explanation Evaluation Criteria: Conceptual understanding of the problem Logical reasoning and approach Model training methodology (not just accuracy) Clarity and correctness of explanation during the interview Team collaboration