The third year opens with the program’s core machine-learning and networking courses alongside software engineering, plus the student’s first professional elective — a choice among automata/compiler theory, object-oriented analysis and design, soft computing, or IoT. A skill-enhancement full-stack web development course and a Flutter-based UI tinkering lab add practical breadth, and the community-service internship from the prior year is formally evaluated in this term.
Semester load: roughly 15 lecture, 1 tutorial and 10 practical hours per week, totaling 23 credits.
Subjects
Machine Learning
- Unit 1: Machine-learning paradigms, the modeling pipeline, and dataset considerations
- Unit 2: Proximity-based models such as k-nearest neighbors
- Unit 3: Decision-tree and Bayes-rule-based classifiers
- Unit 4: Linear discriminants — perceptrons, SVMs, logistic regression, and multi-layer perceptrons
- Unit 5: Clustering approaches, including k-means, fuzzy c-means, and spectral clustering
Computer Networks
- Unit 1: Network types, topologies, and the OSI/TCP-IP reference models
- Unit 2: Data-link layer framing, error control, and sliding-window protocols
- Unit 3: Media access control schemes and Ethernet standards
- Unit 4: Network-layer routing algorithms, congestion control, and IPv4/IPv6
- Unit 5: Transport-layer protocols (UDP/TCP) and application-layer services like HTTP, email, and DNS
Software Engineering
- Unit 1: Software life-cycle models from waterfall through agile and spiral approaches
- Unit 2: Project management, effort estimation, and requirements specification
- Unit 3: Software design principles, agile practices, and user-interface design
- Unit 4: Coding practices, testing strategies, and software quality standards
- Unit 5: CASE tools, software maintenance, and software reuse
Professional Elective-I options:
students choose one of Automata Theory & Compiler Design, Object Oriented Analysis and Design, Soft Computing, Internet of Things, or an approved NPTEL/SWAYAM course.
Automata Theory & Compiler Design
- Unit 1: Regular expressions, finite automata, and their equivalence
- Unit 2: Context-free grammars and pushdown automata
- Unit 3: Lexical analysis and top-down parsing
- Unit 4: Bottom-up parsing and syntax-directed translation
- Unit 5: Intermediate code generation and code optimization
Object Oriented Analysis and Design
- Unit 1: Managing complexity in large software systems
- Unit 2: UML fundamentals and structural modeling
- Unit 3: Class/object diagrams and advanced structural constructs
- Unit 4: Behavioral modeling — use cases, interactions, and activity diagrams
- Unit 5: Advanced behavioral and architectural modeling (state charts, components, deployment)
Soft Computing
- Unit 1: Neural network basics and biological inspiration
- Unit 2: Perceptron learning and backpropagation networks
- Unit 3: Fuzzy sets, relations, and membership functions
- Unit 4: Fuzzy inference systems and neuro-fuzzy hybrids
- Unit 5: Genetic algorithms and genetic-fuzzy hybrid systems
Internet of Things
- Unit 1: IoT overview, M2M communication, and connectivity principles
- Unit 2: Business models, layered IoT architectures, and standardization
- Unit 3: Web connectivity protocols for connected devices
- Unit 4: Data acquisition, organization, and business-process integration
- Unit 5: Cloud-based storage and computing for IoT, plus sensing/RFID technology
Machine Learning Lab
- Central-tendency and dispersion computations, and preprocessing techniques
- Implementing KNN, decision tree, and random forest classifiers
- Naïve Bayes, SVM, and multi-layer perceptron classification exercises
- Regression algorithms and clustering (k-means and related methods)
- Total: 3 practical hours per week, 1.5 credits
Computer Networks Lab
- Framing, checksum, and error-correction coding exercises
- Sliding-window and stop-and-wait protocol simulations
- Routing algorithm implementation (Dijkstra, distance-vector)
- Packet analysis with Wireshark and network scanning with Nmap
- NS2-based simulation of packet loss, congestion, and throughput
- Total: 3 practical hours per week, 1.5 credits
Full Stack Development-1 (Skill Enhancement Course)
- HTML structuring — lists, links, images, tables, forms, and frames
- CSS styling, selector types, and the box model
- JavaScript fundamentals — I/O, conditional logic, loops, and built-in/user-defined objects
- Functions, event handling, and form validation
- An introduction to Node.js
- Total: 1 tutorial and 2 practical hours per week, 2 credits
User Interface Design using Flutter (Tinkering Lab)
- Dart language basics and Flutter widget exploration
- Layout composition using Row, Column, and Stack widgets
- Responsive design and navigation between screens
- State management and custom widget/theme styling
- Form validation, animation, and REST API data fetching
- Total: 2 practical hours per week, 1 credit
Note: the Community Service Project Internship completed the previous summer is formally evaluated this semester, and students may alternatively take Entrepreneurship Development & Venture Creation in place of Open Elective-I.