Comprehensive Course Structure
The Computer Science program at Universal Ai University Maharashtra is structured over eight semesters, with each semester comprising core subjects, departmental electives, science electives, and laboratory sessions. This structure ensures a balanced blend of theoretical knowledge and practical application.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Discrete Mathematics | 3-0-0-3 | - |
| 1 | PH101 | Physics for Computer Science | 3-0-0-3 | - |
| 1 | MA101 | Calculus I | 3-0-0-3 | - |
| 1 | CS103 | Computer Organization | 3-0-0-3 | - |
| 1 | CS104 | Lab: Introduction to Programming | 0-0-3-1 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Digital Logic Design | 3-0-0-3 | - |
| 2 | MA201 | Calculus II | 3-0-0-3 | MA101 |
| 2 | CS203 | Database Systems | 3-0-0-3 | - |
| 2 | CS204 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS205 | Lab: Data Structures & Algorithms | 0-0-3-1 | CS201 |
| 3 | CS301 | Operating Systems | 3-0-0-3 | CS204 |
| 3 | CS302 | Computer Networks | 3-0-0-3 | CS201 |
| 3 | MA301 | Probability and Statistics | 3-0-0-3 | MA101 |
| 3 | CS303 | Software Engineering | 3-0-0-3 | CS204 |
| 3 | CS304 | Web Technologies | 3-0-0-3 | CS204 |
| 3 | CS305 | Lab: Operating Systems | 0-0-3-1 | CS301 |
| 4 | CS401 | Machine Learning | 3-0-0-3 | MA301 |
| 4 | CS402 | Cryptography and Network Security | 3-0-0-3 | CS302 |
| 4 | CS403 | Data Mining | 3-0-0-3 | MA301 |
| 4 | CS404 | Mobile Application Development | 3-0-0-3 | CS204 |
| 4 | CS405 | Lab: Machine Learning | 0-0-3-1 | CS401 |
| 5 | CS501 | Advanced Algorithms | 3-0-0-3 | CS201 |
| 5 | CS502 | Artificial Intelligence | 3-0-0-3 | CS401 |
| 5 | CS503 | Distributed Systems | 3-0-0-3 | CS301 |
| 5 | CS504 | Human-Computer Interaction | 3-0-0-3 | - |
| 5 | CS505 | Lab: Distributed Systems | 0-0-3-1 | CS503 |
| 6 | CS601 | Research Methodology | 2-0-0-2 | - |
| 6 | CS602 | Internship (Summer) | 0-0-0-10 | - |
| 7 | CS701 | Capstone Project I | 0-0-0-6 | - |
| 7 | CS702 | Advanced Topics in AI | 3-0-0-3 | CS502 |
| 7 | CS703 | Specialized Elective I | 3-0-0-3 | - |
| 8 | CS801 | Capstone Project II | 0-0-0-6 | - |
| 8 | CS802 | Specialized Elective II | 3-0-0-3 | - |
| 8 | CS803 | Final Project Presentation | 0-0-0-3 | - |
Advanced Departmental Electives
Departmental electives provide students with the opportunity to delve deeper into specialized areas of interest. Here are detailed descriptions of several advanced courses:
Machine Learning (CS401)
This course covers supervised and unsupervised learning techniques, including decision trees, neural networks, clustering algorithms, and reinforcement learning. Students will implement models using Python libraries like scikit-learn and TensorFlow. The course emphasizes practical applications in image recognition, natural language processing, and recommendation systems.
Cryptography and Network Security (CS402)
This elective explores encryption methods, hash functions, digital signatures, and secure communication protocols. Students will study both classical and modern cryptographic algorithms, including RSA, AES, and elliptic curve cryptography. Practical labs involve implementing secure network architectures and analyzing vulnerabilities in real-world systems.
Data Mining (CS403)
Focused on extracting patterns from large datasets, this course introduces students to data preprocessing, association rule mining, classification, regression, and clustering techniques. Students will use tools like Weka, KNIME, and Python-based libraries to perform exploratory data analysis and build predictive models.
Mobile Application Development (CS404)
This course provides hands-on experience in developing mobile applications for Android and iOS platforms. Topics include UI/UX design, native app development frameworks, API integration, and cloud services. Students will create functional apps that address real-world problems, with an emphasis on user-centric design principles.
Advanced Algorithms (CS501)
This course focuses on advanced algorithmic techniques such as dynamic programming, graph algorithms, approximation algorithms, and complexity theory. Students will solve challenging computational problems using mathematical proofs and algorithmic thinking. The course includes practical implementation challenges and competitive programming contests.
Artificial Intelligence (CS502)
This elective covers advanced topics in AI, including neural networks, deep learning, reinforcement learning, and natural language processing. Students will implement AI systems using frameworks like PyTorch and TensorFlow. The course includes hands-on labs involving image classification, sentiment analysis, and chatbot development.
Distributed Systems (CS503)
This course explores the architecture of distributed systems, including consensus protocols, fault tolerance, and distributed databases. Students will implement distributed applications using tools like Apache Kafka, Docker, and Kubernetes. Labs involve building scalable systems that can handle high throughput and low latency.
Human-Computer Interaction (CS504)
This course focuses on designing user-friendly interfaces and understanding human behavior in digital environments. Students will conduct usability studies, prototype interactive systems, and evaluate interface effectiveness using both qualitative and quantitative methods. The course includes real-world case studies from leading tech companies.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to enhance student engagement and practical understanding. Mini-projects are introduced starting from the second year, allowing students to apply theoretical knowledge in realistic settings. These projects often involve collaboration with industry partners or faculty-led initiatives.
For final-year capstone projects, students select topics aligned with their interests and career goals. They work closely with assigned faculty mentors throughout the process, receiving guidance on research methodologies, technical implementation, and presentation skills. Projects are evaluated based on innovation, technical depth, and real-world applicability.