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The curriculum at Polaris School of Technology is fundamentally different from other new-age tech schools because it is designed around building engineers, not just training candidates. While many modern tech programs focus on short-term upskilling or interview preparation, Polaris follows a Work-Integrated B.Tech in Product Engineering, where academics and real-world engineering run in parallel from Day 1. Students don't “learn first and work later”, they code, build, test, deploy, and iterate on real systems throughout all four years. The curriculum is project-first, not exam-first. Instead of optimizing for marks, it optimizes for outcomes like system design ability, problem-solving, ownership, and production-ready skills. With 5,000+ hours of hands-on coding, internships starting in Year 1, and exposure to Bangalore's tech ecosystem, students gain industry instincts early, understanding constraints, trade-offs, and accountability through experience, not theory. Another key difference is depth and structure. Each year increases industry complexity, from core programming and web development to AI/ML, cloud, DevOps, and finally self-directed deep-tech builds like autonomous systems or generative AI. Learning happens in small, peer-driven cohorts, ensuring close mentorship and high standards

The B.Tech in Computer Science & Engineering (AI & ML) at Polaris School of Technology, awarded by Medhavi Skills University (UGC Recognized), is built on a Living Syllabus. This means while the core engineering credits are fixed, the specific tools and libraries are updated in real-time by industry founders to match current global tech standards (e.g., swapping older libraries for the latest AI frameworks).

Year 1: Foundation in Programming & Web Development

• Technical Focus: Front End Web Development, Foundations of Programming (FOCP), Java & Data Structures and Algorithms (DSA), Introduction to Back-End Development, Advanced DSA, MERN Stack (MongoDB, Express, React, Node.js).

• Outcome: Ability to build full-stack applications, contribute to global Open Source repositories, and secure first-year internships with high-growth startups.

Year 2: Industry-Ready Specialization

• Technical Focus: AI/ML Fundamentals, Advanced Back-End Systems, Introduction to Machine Learning, Data Science & Analytics, AR/VR Development, AI-Augmented Development, Introduction to Deep Learning, and AI Ethics & Responsibility.

• Outcome: Proficiency in building data-driven tools, ML classifiers, and VR projects.

Year 3: Advanced AI/ML Engineering

• Technical Focus: Applied AI/ML, Generative AI, Cloud Computing & DevOps (AWS/Azure/Docker), Neural Networks, Advanced DSA, and Natural Language Processing (NLP).

• Outcome: Capacity to train specialised AI models, deploy scalable cloud applications, and architect production-grade intelligent systems used in enterprise environments.

Year 4: Specialized Deep-Tech & Launch

• Technical Focus: Developing Custom LLMs (ChatGPT-style), Drone Programming, Smart Home & IoT Systems, Quantum Computing, Reinforcement Learning, and Autonomous Robotics.

• Outcome: Graduation with a diverse Product Portfolio and the opportunity to launch a venture through the PST Tech Combinator ($10,000 Seed Fund) or join firms as a Senior Product Engineer.

The official curriculum for the B.Tech in Computer Science & Engineering (AI & ML) at Polaris School of Technology, awarded by Medhavi Skills University, is structured to bridge the gap between academic theory and high-end tech industry requirements. The four-year programme is anchored by a 5,000-hour coding mandate and follows the NEP 2020 WISE (Work-Integrated Skill-Based Higher Education) model.

Year 1 (The Seed): Focuses on core Computer Science foundations, including Java, Advanced Data Structures & Algorithms (DSA), and Full-Stack MERN development, aimed at enabling students to secure early internships and contribute to global Open Source projects.

Year 2 (The Growth): Transitions into specialisation with AI/ML fundamentals, Data Science & Analytics, and AI-Augmented Development, while integrating real-world industry sprints.

Year 3 (The Maestro): Covers advanced technical domains such as Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), and Cloud Computing/DevOps (AWS/Azure/Docker).

Year 4 (The Launch): Allows for deep-tech electives in Autonomous Systems, Robotics, or IoT, culminating in high-stakes product deployment or startup incubation through the PST Tech Combinator.

Progress is measured through shipped work and portfolios, ensuring graduates leave as capable engineers, not just degree holders.