Syllabus

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Computer Architecture

Definition: Computer Architecture studies the design, organization, and performance of computer systems, including processor, memory, and I/O structures.

Module 1: Basic Concepts

• Functional units.
• Bus structures, performance.

Module 2: Instruction Set

• Formats, addressing modes.
• RISC vs CISC.

Module 3: CPU Design

• ALU, control unit.
• Hardwired/microprogrammed control.

Module 4: Memory Hierarchy

• Cache, mapping.
• Virtual memory.

Module 5: I/O & Pipelining

• Interrupts, DMA.
• Pipeline hazards.

Module 6: Advanced Topics

• Multiprocessors.
• Parallel processing.

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Design and Analysis of Algorithms

Definition: Design and Analysis of Algorithms focuses on algorithmic paradigms, complexity analysis, and efficient problem-solving strategies.

Module 1: Fundamentals

• Asymptotic notations.
• Recurrences.

Module 2: Divide and Conquer

• Merge/quick sort.
• Strassen’s multiplication.

Module 3: Greedy Method

• Knapsack, Huffman.
• Activity selection.

Module 4: Dynamic Programming

• 0/1 knapsack.
• LCS, matrix chain.

Module 5: Graph Algorithms

• BFS/DFS, shortest paths.
• MST.

Module 6: Complexity

• P, NP, NP-complete.
• Backtracking.

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Operating System

Definition: Operating System covers process management, memory allocation, file systems, and synchronization in computing environments.

Module 1: Introduction

• OS functions, types.
• System calls.

Module 2: Processes & Threads

• Process states, scheduling.
• Multithreading.

Module 3: Synchronization

• Critical section.
• Semaphores, monitors.

Module 4: Deadlocks

• Prevention, avoidance.
• Detection, recovery.

Module 5: Memory Management

• Paging, segmentation.
• Demand paging.

Module 6: File & I/O

• File system.
• Disk scheduling.

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Soft Computing

Definition: Soft Computing integrates fuzzy logic, neural networks, and evolutionary algorithms for handling uncertainty and approximation.

Module 1: Introduction

• Soft vs hard computing.
• Applications.

Module 2: Fuzzy Logic

• Fuzzy sets, operations.
• Fuzzy inference.

Module 3: Neural Networks

• Perceptron, backpropagation.
• Architectures.

Module 4: Genetic Algorithms

• Operators, selection.
• Schema theorem.

Module 5: Hybrid Systems

• Neuro-fuzzy.
• Genetic-neural.

Module 6: Applications

• Optimization, control.
• Pattern recognition.

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Constitution

Definition: Constitution introduces the Indian Constitution, fundamental rights, duties, and governance structure.

Module 1: Overview

• Making, preamble.
• Salient features.

Module 2: Rights

• Fundamental rights.
• Equality, freedom.

Module 3: Principles & Duties

• Directive principles.
• Fundamental duties.

Module 4: Government

• Executive, legislature.
• Federalism.

Module 5: Judiciary

• Courts, review.

Module 6: Amendments

• Procedure.
• Emergencies.

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Biology

Definition: Biology covers classification, genetics, biomolecules, and biological processes relevant to data science and engineering.

Module 1: Introduction

• Biology as science.
• Observations in biology.

Module 2: Classification

• Hierarchy, criteria.
• Model organisms.

Module 3: Genetics

• Mendel’s laws.
• Gene mapping, disorders.

Module 4: Biomolecules

• Sugars, proteins.
• DNA/RNA, lipids.

Module 5: Enzymes

• Kinetics, regulation.

Module 6: Applications

• Biotechnology basics.

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Artificial Intelligence

Definition: Artificial Intelligence explores problem-solving, knowledge representation, reasoning, planning, learning, and expert systems.

Unit 1: Basics of AI

• Definition, history, domains.
• AI problems, state space.
• Examples: TSP, syntax analysis.
• Issues, assumptions, techniques.
• Criteria for success.

Unit 2: Search and Planning

• Control strategies.
• Uninformed Search: DFS, BFS, IDDFS.
• Heuristic Search: Generate & Test, Hill Climbing, Best First/A, AO.
• Constraint Satisfaction, Means-Ends Analysis, Simulated Annealing.

Unit 3: Knowledge Representation Techniques and Reasoning

• Propositional and Predicate Logic: Syntax, Semantics.
• Resolution, Semantic nets, Frames.
• Conceptual Graphs, Scripts.
• Bayes’ Theorem, Dempster-Shafer Theory.
• Fuzzy Reasoning, Temporal Reasoning.

Unit 4: Planning & Learning

• Planning in Situational Calculus.
• Partial Order Planning.
• Learning: By Examples, Analogy, Explanation-Based.
• Neural Nets, Genetic Algorithms.
• Architecture of Expert Systems (Rule-Based and Non-Rule-Based).

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