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Fees
₹8,50,000
Placement
94.5%
Avg Package
₹7,50,000
Highest Package
₹25,00,000
Fees
₹8,50,000
Placement
94.5%
Avg Package
₹7,50,000
Highest Package
₹25,00,000
Seats
120
Students
120
Seats
120
Students
120
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Mathematics for AI | 3-1-0-4 | None |
| 1 | CS102 | Introduction to Programming | 3-0-0-3 | None |
| 1 | CS103 | Engineering Graphics & Design | 2-1-0-3 | None |
| 1 | PH101 | Physics for Computer Science | 3-1-0-4 | None |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS102 |
| 2 | CS202 | Digital Logic Design | 3-1-0-4 | None |
| 2 | CS203 | Computer Organization | 3-1-0-4 | CS202 |
| 2 | MA201 | Probability and Statistics | 3-1-0-4 | None |
| 3 | CS301 | Operating Systems | 3-1-0-4 | CS201 |
| 3 | CS302 | Database Management Systems | 3-1-0-4 | CS201 |
| 3 | CS303 | Introduction to Machine Learning | 3-1-0-4 | MA201 |
| 3 | EC301 | Signals and Systems | 3-1-0-4 | PH101 |
| 4 | CS401 | Advanced Machine Learning | 3-1-0-4 | CS303 |
| 4 | CS402 | Deep Learning Architectures | 3-1-0-4 | CS401 |
| 4 | CS403 | Reinforcement Learning | 3-1-0-4 | CS401 |
| 4 | CS404 | Natural Language Processing | 3-1-0-4 | CS303 |
| 5 | CS501 | Computer Vision | 3-1-0-4 | CS402 |
| 5 | CS502 | AI Ethics and Governance | 3-1-0-4 | None |
| 5 | CS503 | Robotics and Intelligent Systems | 3-1-0-4 | CS401 |
| 5 | CS504 | AI for Healthcare | 3-1-0-4 | CS401 |
| 6 | CS601 | Cybersecurity and AI | 3-1-0-4 | CS503 |
| 6 | CS602 | Human-Computer Interaction | 3-1-0-4 | None |
| 6 | CS603 | AI in Finance | 3-1-0-4 | CS501 |
| 6 | CS604 | Research Methodology | 2-1-0-3 | None |
| 7 | CS701 | Mini Project I | 0-0-6-3 | CS401 |
| 7 | CS702 | Mini Project II | 0-0-6-3 | CS501 |
| 8 | CS801 | Final Year Thesis/Capstone | 0-0-12-6 | CS702 |
The following departmental elective courses are designed to offer depth and specialization:
This course delves into advanced concepts in machine learning, including ensemble methods, online learning, and model selection. Students explore how algorithms like Random Forests, Gradient Boosting Machines, and Support Vector Regression perform in practical scenarios.
This course focuses on building and optimizing deep neural networks, covering architectures such as CNNs, RNNs, LSTM, GRU, Transformers, and GANs. Practical implementation using TensorFlow and PyTorch is emphasized.
Students learn the principles of reinforcement learning, including Markov Decision Processes, Q-learning, Policy Gradient Methods, and Actor-Critic Algorithms. The course integrates theory with hands-on experimentation in simulated environments.
This elective explores text processing techniques, word embeddings, sequence modeling, and transformer-based architectures like BERT and GPT. Applications include machine translation, summarization, sentiment analysis, and chatbots.
This course introduces students to image processing, feature extraction, object detection, segmentation, and 3D reconstruction using computer vision techniques. It includes practical labs on OpenCV and deep learning frameworks.
Students examine ethical issues in AI deployment, including bias, fairness, transparency, privacy, and regulation. This course prepares them to navigate the societal implications of AI technologies responsibly.
This elective combines robotics with AI to build intelligent systems that can perceive, reason, and act autonomously. Topics include motion planning, control systems, sensor fusion, and multi-agent coordination.
This course explores how AI is transforming healthcare through diagnostics, drug discovery, personalized medicine, and robotic surgery. It includes case studies of real-world applications in hospitals and research institutions.
Students study how AI techniques are used to enhance cybersecurity measures, including anomaly detection, intrusion prevention, and adversarial machine learning. The course also covers ethical hacking and threat modeling.
This course examines the design of interactive systems that integrate AI technologies. It includes user research, prototyping, usability testing, and designing interfaces for AI applications like virtual assistants and recommendation engines.
Students learn how AI is revolutionizing financial services through algorithmic trading, risk management, fraud detection, and robo-advisory systems. The course includes exposure to financial datasets and tools like QuantLib and Bloomberg Terminal.
This foundational course equips students with research skills essential for pursuing advanced studies or industry innovation. It covers literature review, hypothesis formulation, experimental design, data analysis, and academic writing.
The AI program at Bipin Tripathi Kumaon Institute Of Technology emphasizes project-based learning as a cornerstone of education. This pedagogical approach encourages students to apply theoretical knowledge to real-world problems, fostering innovation and collaboration.
Mini-projects are introduced in the seventh semester, allowing students to work on small-scale research or development tasks under faculty supervision. These projects typically span 3-4 months and culminate in presentations and peer reviews.
The final-year thesis or capstone project represents a significant milestone. Students choose topics aligned with their interests or industry demands, working closely with faculty mentors throughout the process. Projects often result in patents, open-source contributions, or startup ideas.
Evaluation criteria for these projects include technical depth, innovation, presentation quality, peer feedback, and documentation standards. Students are encouraged to present their work at national conferences and publish papers in journals.
