Syllabus

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Database Management Systems

Definition: Database Management Systems deals with data modeling, relational databases, SQL, normalization, and transaction management for efficient data storage and retrieval.

Module 1: Database Concepts

• Introduction to DBMS, data models.
• Architecture, data independence.

Module 2: ER Model

• Entities, attributes, relationships.
• ER diagrams, mapping to relational model.

Module 3: Relational Algebra and SQL

• Relational operations.
• SQL queries, DDL, DML.

Module 4: Normalization

• Functional dependencies.
• Normal forms up to BCNF.

Module 5: Transaction Processing

• ACID properties, concurrency control.
• Recovery techniques.

Module 6: Advanced Topics

• Indexing, query optimization.
• NoSQL basics.

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Constitution

Definition: Constitution covers the Indian Constitution, its features, fundamental rights, duties, directive principles, and governance structure.

Module 1: Introduction

• Historical background, making of the Constitution.
• Preamble and salient features.

Module 2: Fundamental Rights

• Right to equality, freedom, against exploitation.
• Right to religion, cultural and educational rights.

Module 3: Directive Principles and Duties

• Directive Principles of State Policy.
• Fundamental Duties.

Module 4: Union and State Government

• President, Prime Minister, Parliament.
• Governor, Chief Minister, State Legislature.

Module 5: Judiciary

• Supreme Court, High Courts.
• Judicial review.

Module 6: Amendments and Emergency

• Amendment procedure.
• Emergency provisions.

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Machine Learning

Definition: Machine Learning explores algorithms that enable systems to learn from data, including supervised, unsupervised, and reinforcement learning techniques.

Module 1: Introduction

• Types of machine learning.
• Applications, learning models.

Module 2: Supervised Learning

• Regression: linear, polynomial.
• Classification: KNN, Naive Bayes.

Module 3: Decision Trees and Ensemble

• Decision trees, random forest.
• Boosting, bagging.

Module 4: Neural Networks

• Perceptron, backpropagation.
• Introduction to deep learning.

Module 5: Unsupervised Learning

• Clustering: K-means.
• Dimensionality reduction: PCA.

Module 6: Evaluation and Advanced Topics

• Metrics, cross-validation.
• Reinforcement learning basics.

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Object Oriented Programming (OOPS)

Definition: Object Oriented Programming emphasizes programming using classes, objects, inheritance, polymorphism, and encapsulation for modular code design.

Module 1: OOP Concepts

• Classes, objects, abstraction.
• Encapsulation, constructors.

Module 2: Inheritance and Polymorphism

• Types of inheritance.
• Method overriding, overloading.

Module 3: Interfaces and Packages

• Abstract classes, interfaces.
• Packages, access modifiers.

Module 4: Exception Handling

• Try-catch, throw, throws.
• Custom exceptions.

Module 5: Multithreading

• Thread life cycle.
• Synchronization.

Module 6: GUI and Advanced

• AWT/Swing basics.
• Generics, collections.

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Signals & Systems

Definition: Signals & Systems introduces the fundamental concepts of signals, their properties, and system behaviors, with emphasis on time-domain and frequency-domain analysis for continuous and discrete systems.

Module 1: Introduction to Signals and Systems

• Signals and systems as seen in everyday life, and in various branches of engineering and science.
• Signal properties: periodicity, absolute integrability, determinism and stochastic character.
• Some special signals of importance: the unit step, the unit impulse, the sinusoid, the complex exponential, some special time-limited signals.
• Continuous and discrete time signals, continuous and discrete amplitude signals.
• System properties: linearity (additivity and homogeneity), shift-invariance, causality, stability, realizability.
• Examples.

Module 2: Behavior of Continuous and Discrete-Time LTI Systems

• Impulse response and step response, convolution.
• Input-output behavior with aperiodic convergent inputs, cascade interconnections.
• Characterization of causality and stability of LTI systems.
• System representation through differential equations and difference equations.
• State-space Representation of systems, State-Space Analysis, Multi-input, multi-output representation.
• State Transition Matrix and its Role.
• Periodic inputs to an LTI system, the notion of a frequency response and its relation to the impulse response.

Module 3: Fourier, Laplace and Z- Transforms

• Fourier series representation of periodic signals, Waveform Symmetries, Calculation of Fourier Coefficients.
• Fourier Transform, convolution/multiplication and their effect in the frequency domain, magnitude and phase response, Fourier domain duality.
• The Discrete-Time Fourier Transform (DTFT) and the Discrete Fourier Transform (DFT).
• Parseval’s Theorem.
• Review of the Laplace Transform for continuous time signals and systems, system functions, poles and zeros of system functions and signals, Laplace domain analysis, solution to differential equations and system behavior.
• The z-Transform for discrete time signals and systems, system functions, poles and zeros of systems and sequences, z-domain analysis.

Module 4: Sampling and Reconstruction

• The Sampling Theorem and its implications.
• Spectra of sampled signals.
• Reconstruction: ideal interpolator, zero-order hold, first-order hold.
• Aliasing and its effects.
• Relation between continuous and discrete time systems.
• Introduction to the applications of signal and system theory: modulation for communication, filtering, feedback control systems.

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Theory of Computation

Definition: Theory of Computation studies abstract models of computation, including automata, grammars, and computability.

Module 1: Finite Automata

• DFA, NFA.
• Regular expressions.

Module 2: Context-Free Grammars

• Pushdown automata.
• CFG, parsing.

Module 3: Turing Machines

• TM design.
• Variants of TM.

Module 4: Decidability

• Decidable and undecidable problems.
• Halting problem.

Module 5: Complexity

• P, NP, NP-complete.
• Reductions.

Module 6: Advanced Models

• Post correspondence problem.
• Recursive languages.

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Biology

Definition: Biology for engineers introduces biological principles, cell structure, genetics, and biotechnology applications.

Module 1: Cell Biology

• Cell structure, organelles.
• Cell division.

Module 2: Genetics

• DNA, RNA, protein synthesis.
• Mendelian genetics.

Module 3: Microbiology

• Bacteria, viruses.
• Immune system.

Module 4: Physiology

• Human systems overview.
• Enzymes, metabolism.

Module 5: Biotechnology

• Genetic engineering.
• Applications.

Module 6: Ecology

• Ecosystems, biodiversity.
• Environmental biology.

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Universal Human Values

Definition: Universal Human Values fosters ethical understanding, harmony in self, family, society, and nature through value-based living.

Module 1: Introduction

• Need for value education.
• Self-exploration.

Module 2: Harmony in Self

• Understanding human being.
• Harmony in feelings.

Module 3: Harmony in Family and Society

• Family as basic unit.
• Justice in relationships.

Module 4: Harmony in Nature

• Co-existence with nature.
• Ecological balance.

Module 5: Professional Ethics

• Values in profession.
• Competence and commitment.

Module 6: Implications

• Holistic development.
• Vision for humane society.

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