DAU’s B.Tech. in Computer Science and Artificial Intelligence (CSAI) is a four-year undergraduate program designed for students who want strong foundations in Computer Science together with modern Artificial Intelligence. Computing systems now drive innovation across technological sectors, and AI increasingly enables pattern discovery, prediction, and decision-making across domains such as vision, speech, natural language processing, robotics, healthcare, business, and finance.

The CSAI program therefore combines the theory, design, and application of computing systems with the concepts, tools, and technologies needed to create intelligent systems. The curriculum is designed to prepare students for both research and industry by building mathematical maturity, systems understanding, and AI-oriented problem-solving capability.

The curriculum is structured to build foundations first and specialization later. The first three to four semesters emphasize core Computer Science and Mathematics, especially topics relevant to Artificial Intelligence and Machine Learning. Once these foundations are established, the program expands into advanced AI areas such as traditional AI, machine learning, deep learning, trustworthy AI, reinforcement learning, and large-scale data handling.

The curriculum also includes experiential learning through three compulsory project or internship components: a Rural Internship after Semester 3, a Summer Research or Industry Internship after Semester 5, and a final-semester B.Tech. Project or Internship.

Course Categories

• Institute Core (IC) Mandatory courses common to B.Tech. students at DAU.
• Program Core (PC) Mandatory courses specific to the CSAI program.
• Program Electives (PE) Elective courses designated for CSAI students.
• Free Electives (FE) Open elective choices available beyond the program baskets.

Broad Curriculum Components

• Foundation and Core Courses.
  Students build strength in programming, data structures, algorithms, operating systems, databases, mathematics, machine learning, and AI foundations.
• Elective Courses.
  From the middle semesters onward, students can broaden or deepen their exposure through carefully structured elective baskets.
• Internships and Project Work.
  The programme integrates social exposure, research or industry engagement, and a substantial final-year project or internship.
• Co-curricular and Exploratory Components.
  The curriculum includes co-curricular activities, reading and writing components, exploratory projects, and a certificate course.

Electives and Advanced Pathways

The elective design allows students to build depth in AI while retaining exposure to rigorous Computer Science and adjacent technical areas.

Students must complete:

• a minimum of three elective courses from AI and its Applications
• a minimum of two elective courses from Computer Science and Engineering
• the remaining three program electives from any of the five elective baskets listed below.

Elective Baskets (Tentative)

1. AI and its Applications

   Information Retrieval, Natural Language Processing, Generative AI, Large Language Models, MLOps, LLMOps, AI in Healthcare, Computer Vision, Digital Image Processing, Speech Processing, Recommendation Systems, Adversarial ML, Quantum ML, Robotics, Control of Autonomous Systems.

2. Computer Science and Engineering

   Theory of Computation, Computer Networks, Advanced Data Structures, Algorithmic Graph Theory, Compiler Design, Formal Specification and Verification, Randomized Algorithms, Approximation Algorithms, Parallel Algorithms, Cryptography, HCI.

3. Computational Science

   High Performance Computing, Distributed Computing, Cloud Computing, GPU Computing, Quantum Computing, Modeling and Simulation.

4. Mathematics

   Numerical and Computational Methods, Game Theory, Combinatorial Optimization, Operations Research, Algebraic Structures.

5. Data Science and Engineering

   Signals and Systems, Data Analysis and Visualization, Data Security and Privacy, Advanced DBMS, Bayesian Data Analysis, Time Series Analysis, Business Data Analysis, Financial Data Analysis.

Programme Outcomes (POs)

After the successful completion of the B.Tech. in Computer Science and Artificial Intelligence (CSAI) programme, students will be able to:

PO No.	Programme Outcomes
PO1	Engineering and Computing Knowledge: Apply knowledge of mathematics, statistics, science, computing fundamentals, and artificial intelligence to solve complex engineering and computational problems.
PO2	Problem analysis: Identify, formulate, review relevant literature, and analyze complex computing and AI problems using principles of mathematics, data analysis, and computer science to reach substantiated conclusions.
PO3	Design/development of solutions: Design and develop software systems, intelligent models, and data-driven solutions that meet specified needs with due consideration for usability, safety, societal context, and sustainability.
PO4	Conduct investigations of complex problems: Conduct investigations using research methods, experimentation, model evaluation, data interpretation, and evidence-based reasoning to derive valid conclusions for computing and AI systems.
PO5	Modern tool usage: Select and apply appropriate techniques, platforms, programming frameworks, AI/ML libraries, data tools, and engineering practices to build and evaluate complex systems, while understanding their limitations.
PO6	The engineer and society: Apply contextual knowledge to assess societal, legal, cultural, accessibility, and human-centered considerations relevant to professional computing and AI practice.
PO7	Environment and sustainability: Understand the environmental and societal impact of computing infrastructure and AI solutions, and make informed choices that support sustainable technological development.
PO8	Ethics: Apply ethical principles and commit to professional responsibilities in the design, deployment, and use of software and AI systems, including issues of bias, privacy, fairness, transparency, and safety.
PO9	Individual and team work: Function effectively as an individual and as a member or leader in diverse, multidisciplinary, and collaborative teams.
PO10	Communication: Communicate effectively with technical and non-technical audiences through reports, design documents, visual presentations, and clear oral and written interaction.
PO11	Project management and finance: Apply engineering and management principles to plan, execute, and manage projects in computing and AI, including teamwork, scheduling, resource use, and economic considerations.
PO12	Life-long learning: Recognize the need for independent and life-long learning in response to rapid advances in computer science, artificial intelligence, and related interdisciplinary domains.

The Programme Specific Outcomes (PSOs) set the following goal:

The B.Tech. in Computer Science and Artificial Intelligence (CSAI) programme is designed to enable students to:

PSO No.	Program Specific Outcomes (PSOs)
PSO1	Core Intelligent Systems Design: Apply foundations in algorithms, data structures, databases, operating systems, mathematics, probability, statistics, and optimization to analyze, design, and implement intelligent computing systems.
PSO2	AI Model Development and Deployment: Build, evaluate, and deploy machine learning, deep learning, reinforcement learning, and data-intensive solutions using modern software engineering, big data, and computational tools.
PSO3	Responsible and Applied AI Practice: Develop AI-enabled solutions for real-world and interdisciplinary applications with due attention to fairness, privacy, explainability, robustness, safety, and societal impact.