IIT Bombay B.Tech Computer Science

Introduction to programming fundamentals and computational thinking:

• Basic programming concepts: variables, data types, operators, expressions
• Control structures: conditional statements, loops, and branching
• Functions and modular programming
• Basic data structures: arrays, strings, and records
• Introduction to algorithms and problem-solving approaches
• Simple file handling and input/output operations
• Debugging techniques and program testing methodologies
• Introduction to software development lifecycle

Foundational course connecting computing principles with scientific applications:

• History and evolution of computing and its impact on scientific discovery
• Basic computational thinking and algorithmic problem solving
• Representation of data: numbers, text, images, and scientific data
• Introduction to computational models in science
• Basic simulation techniques for scientific problems
• Introduction to high-performance computing concepts
• Ethical considerations in scientific computing
• Case studies of computing applications in various scientific domains

Introduction to programming paradigms and abstraction techniques:

• Concept of abstraction in programming and problem solving
• Procedural programming paradigm and its principles
• Functional programming concepts: pure functions, immutability
• Object-oriented programming fundamentals: classes, objects, inheritance
• Basic design patterns and their applications
• Modularity and information hiding principles
• Introduction to recursion and recursive problem solving
• Comparative analysis of different programming paradigms

Mathematical foundations essential for computer science:

• Logic and proofs: propositional and predicate calculus
• Set theory: operations, relations, functions
• Mathematical induction and recursion
• Combinatorics: counting principles, permutations, combinations
• Graph theory fundamentals: graphs, trees, connectivity
• Relations: equivalence relations, partial orders
• Boolean algebra and its applications
• Applications to computer science problems and algorithms

Practical implementation of different programming paradigms:

• Implementation of procedural programming concepts
• Functional programming exercises with recursion and higher-order functions
• Object-oriented programming implementation: classes, inheritance, polymorphism
• Comparative analysis of code written in different paradigms
• Problem decomposition approaches for different paradigms
• Testing and debugging techniques specific to each paradigm
• Performance considerations across different programming approaches
• Integration of multiple paradigms in single applications

Intermediate programming concepts and techniques:

• Advanced data structures: linked lists, stacks, queues, trees
• Memory management and dynamic allocation
• File handling and serialization techniques
• Error handling and exception management
• Introduction to software libraries and APIs
• Code optimization techniques
• Introduction to multi-file projects and modular design
• Basic testing frameworks and unit testing concepts

Core data structures and fundamental algorithms:

• Abstract data types and their implementations
• Linear data structures: arrays, linked lists, stacks, queues
• Non-linear data structures: trees (binary trees, heaps, BSTs), graphs
• Hashing techniques and hash table implementations
• Basic sorting and searching algorithms
• Algorithm analysis: time and space complexity, Big O notation
• Recursion and backtracking techniques
• Applications of data structures in problem solving

Logic foundations and applications in computer science:

• Propositional logic: syntax, semantics, truth tables
• Predicate logic: quantifiers, interpretations
• Logical equivalences and normal forms
• Proof techniques: natural deduction, resolution
• Applications in program verification
• Logic in database query languages
• Temporal logic for system specification
• Introduction to model checking concepts

Practical implementation and application of data structures:

• Implementation of linear data structures: arrays, linked lists, stacks, queues
• Implementation of non-linear data structures: trees, heaps, graphs
• Hash table implementation with collision resolution techniques
• Sorting algorithm implementations and performance comparison
• Searching algorithms implementation: binary search, hash-based search
• Memory management for complex data structures
• Application of data structures to solve real-world problems
• Performance analysis and optimization of data structure operations

Theoretical foundations of computation and formal languages:

• Finite automata: DFA, NFA, and their equivalence
• Regular expressions and regular languages
• Context-free grammars and pushdown automata
• Pumping lemmas for regular and context-free languages
• Turing machines and the Church-Turing thesis
• Decidability and undecidable problems
• Computational complexity: P, NP, NP-completeness
• Applications in compiler design and pattern matching

Digital circuit design and implementation:

• Boolean algebra and logic gates
• Combinational circuit design: adders, multiplexers, decoders
• Sequential circuit design: flip-flops, counters, registers
• Finite state machine design and implementation
• Arithmetic circuits and ALU design
• Introduction to hardware description languages (HDL)
• Timing analysis and hazards in digital circuits
• Design optimization techniques for digital circuits

Advanced digital circuit design principles:

• Number representation systems and arithmetic operations
• Combinational circuit minimization techniques
• Synchronous and asynchronous sequential circuits
• Memory elements and storage devices
• Programmable logic devices (PLDs, FPGAs)
• Digital system design methodologies
• Introduction to computer-aided design (CAD) tools for digital circuits
• Verification and testing of digital circuits

Integration of digital design with computer architecture concepts:

• Digital circuit design for computer components
• Basic computer organization: CPU, memory, I/O systems
• Instruction set architecture design principles
• Data path and control unit design
• Memory hierarchy design: cache, main memory
• Introduction to pipelining concepts
• Hardware description language (HDL) for computer design
• Performance evaluation of computer systems

Hands-on implementation of digital circuits and computer components:

• Implementation of basic logic gates and combinational circuits
• Design and implementation of arithmetic circuits
• Sequential circuit implementation: counters, registers
• Finite state machine implementation for control logic
• Memory circuit design and implementation
• Simple CPU design and implementation using HDL
• Simulation and verification of digital circuits
• Performance analysis of implemented digital systems

Practical experience with software system development:

• Implementation of system-level software components
• Memory management techniques and implementations
• File system operations and implementations
• Process management and scheduling algorithms
• Inter-process communication mechanisms
• System call interface implementation
• Debugging and performance analysis of system software
• Integration of multiple system components into cohesive software systems

Practical implementation of digital logic circuits:

• Implementation of basic combinational circuits
• Design and testing of arithmetic circuits
• Sequential circuit implementation: counters, registers
• Finite state machine design and implementation
• Memory circuit design and testing
• Use of hardware description languages (HDL) for circuit design
• Simulation and verification of digital circuits
• Performance analysis and optimization of implemented circuits

Hands-on experience with digital logic design:

• Implementation of Boolean functions using logic gates
• Combinational circuit design: adders, multiplexers, decoders
• Sequential circuit design: flip-flops, counters, registers
• Finite state machine implementation for control applications
• Timing analysis and hazard elimination techniques
• Use of design automation tools for logic design
• Testing and verification of digital circuits
• Optimization of digital circuit implementations

Advanced principles of computer system design:

• Instruction set architecture design and evaluation
• Processor organization: data path and control
• Pipelining techniques and hazard resolution
• Memory hierarchy design: cache, main memory, virtual memory
• I/O systems and device interfaces
• Parallel processing concepts: multicore, multithreading
• Performance evaluation metrics and methodologies
• Emerging trends in computer architecture

Advanced theory of computation and formal languages:

• Finite automata: DFA, NFA, ε-NFA and their equivalence
• Regular expressions and regular grammars
• Context-free grammars and pushdown automata
• Pumping lemmas and closure properties
• Turing machines and variants
• Decidability and the halting problem
• Computational complexity classes: P, NP, NP-completeness
• Applications in compiler design and pattern recognition

Principles and techniques for implementing programming languages:

• Phases of compilation: lexical analysis, parsing, semantic analysis
• Syntax-directed translation and intermediate code generation
• Symbol table management techniques
• Type checking and semantic analysis
• Runtime environment design and memory management
• Code generation and optimization techniques
• Implementation of different programming paradigms
• Introduction to interpreter design and implementation

Practical implementation of embedded systems:

• Microcontroller programming and interfacing
• Sensor and actuator integration with embedded systems
• Real-time operating system concepts and implementation
• Low-power design techniques for embedded systems
• Hardware-software co-design methodologies
• Communication protocols for embedded systems (I2C, SPI, UART)
• Testing and debugging of embedded systems
• Development of complete embedded system applications

Hands-on implementation of language processing systems:

• Implementation of lexical analyzers using tools like lex/flex
• Parser implementation using tools like yacc/bison
• Symbol table implementation and management
• Syntax-directed translation implementation
• Type checking system implementation
• Intermediate code generation techniques
• Code optimization implementations
• Complete compiler/interpreter implementation for a subset language

Practical implementation of operating system components:

• Process management and scheduling algorithm implementation
• Inter-process communication mechanisms implementation
• Memory management techniques implementation
• File system operations and implementation
• Device driver development basics
• Synchronization primitives implementation
• Deadlock handling techniques implementation
• System call interface implementation and extension

Foundations of AI and machine learning algorithms:

• Problem solving by search: uninformed and informed search
• Constraint satisfaction problems and techniques
• Knowledge representation and reasoning
• Machine learning fundamentals: supervised, unsupervised, reinforcement learning
• Classification algorithms: decision trees, SVM, neural networks
• Regression techniques and applications
• Clustering algorithms and evaluation
• Ethical considerations in AI and machine learning

Practical exploration of computer architecture concepts:

• Processor design and implementation using HDL
• Pipelining implementation and hazard handling
• Cache memory design and implementation
• Memory hierarchy performance analysis
• Performance measurement and benchmarking techniques
• Parallel processing implementation experiments
• Computer architecture simulation using tools like gem5, SimpleScalar
• Optimization techniques for specific workloads

Advanced concepts and techniques in artificial intelligence:

• Advanced search algorithms and optimization techniques
• Knowledge representation: semantic networks, frames, ontologies
• Reasoning under uncertainty: Bayesian networks, probabilistic reasoning
• Planning algorithms and techniques
• Natural language processing fundamentals
• Computer vision basics
• Multi-agent systems and game theory applications
• Advanced machine learning integration with AI systems

Comprehensive study of operating system principles and design:

• Operating system structures and services
• Process management: creation, scheduling, synchronization
• Thread management and multithreading models
• Concurrency: critical sections, semaphores, monitors
• Deadlock prevention, avoidance, and detection
• Memory management: paging, segmentation, virtual memory
• File systems: implementation, directory structures, protection
• I/O systems and device drivers

Principles and protocols of computer networking:

• Network architecture and protocol layers
• Physical layer concepts and transmission media
• Data link layer: framing, error detection, MAC protocols
• Network layer: routing algorithms, IP protocol
• Transport layer: TCP, UDP, congestion control
• Application layer protocols: HTTP, DNS, SMTP
• Network security fundamentals
• Wireless and mobile networks concepts

Advanced implementation of operating system components:

• Advanced process scheduling algorithm implementation
• Memory management techniques implementation (paging, segmentation)
• Virtual memory implementation and page replacement algorithms
• File system design and implementation
• Network file system implementation
• Security mechanisms implementation
• Operating system kernel modification and extension
• Performance analysis of implemented OS components

Practical implementation of AI algorithms and systems:

• Implementation of search algorithms (A*, IDA*, etc.)
• Constraint satisfaction problem solvers implementation
• Knowledge representation and reasoning systems
• Machine learning algorithm implementation (classification, clustering)
• Natural language processing applications development
• Basic computer vision applications
• Reinforcement learning implementations
• AI application development for specific problem domains

Practical database system implementation and application development:

• SQL programming and advanced query techniques
• Database design using ER modeling and normalization
• Transaction management implementation
• Indexing techniques implementation (B-trees, hash indexes)
• Query processing and optimization implementation
• Database application development using APIs
• NoSQL database implementation and usage
• Big data processing techniques implementation

Advanced graph theory concepts and applications in computer science:

• Graph representations and basic properties
• Connectivity, paths, and cycles in graphs
• Trees and spanning trees algorithms
• Graph coloring and applications
• Planar graphs and Kuratowski's theorem
• Matching theory and algorithms
• Network flow algorithms (Ford-Fulkerson, Edmonds-Karp)
• Applications in computer networks, social networks, and optimization

Comprehensive introduction to machine learning theory and practice:

• Supervised learning: linear regression, logistic regression
• Support Vector Machines (SVM) and kernel methods
• Decision trees and ensemble methods (random forests, boosting)
• Neural networks and deep learning fundamentals
• Unsupervised learning: clustering, dimensionality reduction
• Reinforcement learning basics
• Learning theory: bias-variance tradeoff, overfitting, regularization
• Practical implementation using machine learning frameworks

Principles and techniques for automated logical reasoning:

• Propositional logic reasoning: DPLL, resolution
• First-order logic reasoning: unification, resolution
• Satisfiability modulo theories (SMT) solvers
• Theorem proving techniques
• Model checking fundamentals
• Decision procedures for specific theories
• Applications in program verification and analysis
• Integration with artificial intelligence systems

Advanced AI programming techniques and specialized topics:

• Advanced search and optimization techniques
• Probabilistic graphical models implementation
• Natural language processing programming techniques
• Computer vision programming applications
• Reinforcement learning programming frameworks
• AI for games and interactive systems
• Knowledge-based system development
• AI system integration and deployment considerations

Advanced concepts in computer networking:

• Network layer protocols: IPv6, multicast routing
• Transport layer enhancements: TCP variants, QUIC
• Network security: IPsec, TLS, firewalls
• Wireless network protocols: 802.11, cellular networks
• Software-defined networking (SDN) concepts
• Network function virtualization (NFV)
• Content distribution networks (CDNs)
• Network measurement and performance analysis

Advanced programming paradigms: functional and logic programming:

• Functional programming concepts: pure functions, immutability
• Higher-order functions and function composition
• Laziness and infinite data structures
• Type systems in functional languages
• Logic programming fundamentals: Horn clauses, unification
• Prolog programming and applications
• Implementation techniques for functional and logic languages
• Applications in artificial intelligence and theorem proving

Culminating project experience integrating knowledge from the entire program:

• Identification of a significant computer science problem or application
• Literature review and background research
• Project planning and requirements specification
• Design of solution architecture and components
• Implementation of the proposed solution
• Testing, validation, and performance evaluation
• Documentation of the project process and outcomes
• Final presentation and defense of the project

Continuation and completion of the B.Tech project:

• Advanced implementation of project components
• Integration of different system modules
• Performance optimization and refinement
• Rigorous testing and validation procedures
• Comparison with existing solutions and state-of-the-art
• Documentation of final system and user manuals
• Preparation of final project report
• Project presentation and defense preparation

Final stage of the B.Tech project with focus on refinement and delivery:

• Final system integration and testing
• User interface refinement and usability testing
• Performance analysis and benchmarking
• Documentation finalization
• Preparation of demonstration materials
• Project presentation and defense
• Evaluation of project outcomes against initial objectives
• Identification of future work and potential improvements

Foundations of cryptographic techniques and protocols:

• Classical ciphers and cryptanalysis
• Information-theoretic security and perfect secrecy
• Symmetric key cryptography: block ciphers, stream ciphers
• Public key cryptography: RSA, Diffie-Hellman, elliptic curves
• Digital signatures and authentication protocols
• Hash functions and message authentication codes
• Key management and distribution
• Applications in secure communication and protocols

Techniques and algorithms for extracting knowledge from large datasets:

• Data preprocessing and cleaning techniques
• Association rule mining and frequent pattern discovery
• Classification algorithms: decision trees, Naive Bayes, SVM
• Clustering algorithms: k-means, hierarchical, density-based
• Anomaly detection techniques
• Web mining and social network analysis
• Text mining and information retrieval
• Big data mining frameworks and tools

Computational techniques for processing and understanding human language:

• Text processing and linguistic fundamentals
• Regular expressions and string algorithms
• Part-of-speech tagging and morphological analysis
• Syntactic parsing techniques
• Semantic analysis and word sense disambiguation
• Machine translation fundamentals
• Sentiment analysis and opinion mining
• Deep learning approaches for NLP tasks