Characterization of distributed systems − Examples − Resource sharing and the Web − Challenges − System models − Architectural and. BE Lecturer Notes IT – MOBILE COMPUTING L T P C 3 0 0 3 . CS – DISTRIBUTED SYSTEMS UNIT I BASIC CONCEPTS. CS Distributed Systems November / December Question Paper CSE 6th Semester Regulation Anna university Trichirapalli. BE/B Tech Degree.

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Labels AI 1 syllabus 6. Data Warehousing and Mining – Syllabus.

To introduce the concept of data mining with in detail coverage of basic tasks, metrics, issues, and implication. Core topics like classification, clustering and association rules are exhaustively dealt with.

To introduce the concept of data warehousing with special emphasis on architecture and design. Alex Bezon, Stephen J. Numerical Methods – Syllabus.

MA/CSE21 Numerical Methods CSE Anna University paper – Fresherscampus

This course gives a complete procedure for solving different kinds of problems occur in engineering numerically. The roots of nonlinear algebraic or transcendental equations, solutions of large system of linear equations and eigenvalue problem of a matrix can be obtained numerically where analytical methods fail to give solution. When huge amounts of experimental data are involved, the methods discussed on interpolation will be useful in constructing approximate polynomial distribuhed represent the data and to find the intermediate values.

The numerical differentiation and integration find application when the systeks in the analytical form is too complicated or the huge amounts of data are given such as series of measurements, observations or some other empirical information. The methods introduced systemms the solution of ordinary differential equations and partial differential equations will be useful in attempting any engineering problem.


Taylor series method — Euler and modified Euler methods — Fourth order Runge — Kutta method for solving first and second order equations — Multistep methods: F, and Wheatley, P. Ltd, New Delhi, L and Systrms, T.

Graphics and Multimedia – Syllabus. Donald Hearn and M.

Chapters 1 to 6; UNIT 2: Chapter 9 — 12, 15, 16 2. Software Engineering – Syllabus. Principles of Compiler Design – Syllabus. To understand, design and implement a parser. To understand, design code generation systmes.

6th Sem Syllabus Online Notes

To understand optimization of codes and runtime environment. Artificial Intelligence aims at developing computer applications, which encompasses perception, reasoning and learning and to provide an in-depth understanding of major techniques used to simulate intelligence. To enable the student to apply these techniques in applications which involve perception, reasoning and learning. Intelligent Agents — Agents and environments – Good behavior — The nature of environments — structure of agents – Problem Solving – problem solving agents — example problems — searching for solutions — uniformed search strategies – avoiding repeated states — searching with partial information.

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Informed search and exploration — Informed search strategies — heuristic function — local search algorithms and optimistic problems — local search in continuous spaces — online search agents and unknown environments – Constraint satisfaction problems CSP — Backtracking search jotes Local search for CSP — Structure of problems – Adversarial Search — Games — Optimal decisions in games — Alpha — Beta Pruning — imperfect real-time decision — games that include an element of chance.


First order logic — representation revisited — Syntax and semantics for first order logic — Using first order logic — Knowledge engineering in first order logic – Inference in First order logic — prepositional versus first order logic — unification and lifting — forward chaining — backward distributde – Resolution – Knowledge representation – Ontological Engineering – Categories and objects — Actions – Simulation and events – Mental events and mental ntoes.

Learning from observations – forms of learning – Inductive learning – Learning decision trees – Ensemble learning – Knowledge in learning — Logical formulation of learning — Explanation based learning — Learning using relevant information — Inductive logic programming – Statistical learning methods – Learning with complete data – Learning with hidden variable – EM algorithm – Instance based learning – Neural networks – Reinforcement learning — Passive reinforcement learning – Active reinforcement learning – Generalization in reinforcement learning.

Communication — Communication as action — Formal grammar for a fragment of English — Syntactic analysis — Augmented grammars — Semantic interpretation — Ambiguity and disambiguation — Discourse understanding — Grammar induction systeems Probabilistic language processing – Probabilistic language models — Information retrieval — Information Extraction — Machine translation.