B.Tech Syllabus (7th Sem Information Technology)
Seventh semester syllabus for Information Technology at Bihar Engineering University
7th Semester Courses
| Paper Code | Paper Title | L | T | P | Credits |
|---|---|---|---|---|---|
| 1057xx | Cryptography & Network Security | 3 | 0 | 0 | 3 |
| 1057xx | Data Mining & Warehousing | 3 | 0 | 0 | 3 |
| 1057xx | Artificial Intelligence | 3 | 0 | 0 | 3 |
Subject Details
Module 1: Introduction to Cryptography
Security goals, services and mechanisms. Attack types. Classical encryption techniques: Symmetric cipher model, Substitution techniques, Transposition techniques.
Module 2: Symmetric Key Cryptography
Block ciphers, Data Encryption Standard (DES), Advanced Encryption Standard (AES), Cipher block modes of operation.
Module 3: Asymmetric Key Cryptography
Public-key cryptography principles, RSA algorithm, Diffie-Hellman key exchange, Elliptic Curve Cryptography (ECC).
Module 4: Message Authentication & Hash Functions
Authentication requirements, Message Authentication Code (MAC), Hash functions, Secure Hash Algorithm (SHA), Digital signatures.
Module 5: Network Security
Authentication applications (Kerberos), Email security (PGP, S/MIME), IP Security (IPSec), Web security (SSL/TLS).
Module 6: System Security
Intruders, Malicious software, Viruses, Firewalls, Security standards.
Module 1: Data Warehousing
Introduction, Multidimensional data model, OLAP operations, Data warehouse architecture, Data warehouse implementation.
Module 2: Data Preprocessing
Data cleaning, Data integration, Data reduction, Data transformation and discretization.
Module 3: Data Mining Primitives
Data mining functionalities, Classification of data mining systems, Data mining task primitives, Integration of data mining system with data warehouse.
Module 4: Association Rule Mining
Frequent itemset mining methods: Apriori algorithm, FP-growth algorithm. Mining various kinds of association rules.
Module 5: Classification & Prediction
Decision tree induction, Bayesian classification, Rule-based classification, Prediction: Linear regression, Non-linear regression.
Module 6: Cluster Analysis
Types of data in cluster analysis, Partitioning methods: k-means, k-medoids. Hierarchical methods, Density-based methods.
Module 1: Introduction
Definition of AI, history, applications. Intelligent agents: environment, rational behavior, types of agents.
Module 2: Problem Solving & Search
State space search, uninformed search (BFS, DFS, Depth-limited), informed search (A* search, Heuristics), Constraint Satisfaction Problems (CSP).
Module 3: Knowledge Representation & Logic
Propositional logic, First-order logic, Inference in FOL, Unification, Resolution. Ontological engineering, Categories and objects.
Module 4: Uncertain Knowledge & Reasoning
Probability theory, Bayes' rule, Bayesian networks, exact and approximate inference. Temporal models, Hidden Markov Models (HMM).
Module 5: Machine Learning
Supervised learning: Decision trees, Neural networks, Support Vector Machines (SVM). Unsupervised learning: Clustering. Reinforcement learning.
Module 6: Natural Language Processing & Robotics
Language models, Information extraction, Machine translation. Robotics: perception, planning, and acting.