CSE (Evening – Fall 2015 / Spring 2016 / Fall 2016)
|Semester 1-1 (23.5 Credits)|
|Course Code||Course Title||Credit Hours|
|HUM 103||Language Composition & Comprehension||3|
|CSE 103||Computer Programming in C||3|
|CSE 104||Computer Programming in C Lab||1.5|
|PHY 101E||Physics for Engineers||3|
|PHY 102||Physics for Engineers Lab||1.5|
|MTH 101E||Geometry, Differential & Integral Calculus||3|
|ECE 101||Basic Electrical Engineering||3|
|ECE 102||Basic Electrical Engineering Lab||1.5|
|ECN 101||Principles of Economics||2|
|ACN 201||Principles of Accounting||2|
|Semester 1-2 (23.5 Credits)|
|Course Code||Course Title||Credit Hours|
|CSE 200||Project Work||2|
|CSE 201||Discrete Mathematics||3|
|CSE 203||Object Oriented Programming Language||3|
|CSE 204||Object Oriented Programming Language Lab||1.5|
|CSE 205||Data Structures||3|
|CSE 206||Data Structures Lab||1.5|
|ECE 201||Electronic Devices & Circuits||3|
|ECE 202||Electronic Devices & Circuits Lab||1.5|
|MTH 103E||Linear Algebra, Vector Analysis & Complex Variables||3|
|IMG 201||Principles of Management||2|
|Semester 2-1 (22.5 Credits)|
|Course Code||Course Title||Credit Hours|
|CSE 208||Algorithms Lab||1.5|
|CSE 209||Numerical Methods||3|
|CSE 231||Digital Logic Design||3|
|CSE 232||Digital Logic Design Lab||1.5|
|CSE 321||Database Systems||3|
|CSE 322||Database Systems Lab||1.5|
|CSE 331||Computer Architecture||3|
|MTH 203E||Differential Equations, Laplace Transforms & Fourier Analysis||3|
|Semester 2-2 (23.5 Credits)|
|Course Code||Course Title||Credit Hours|
|CSE 300||Software Development||2|
|CSE 301||E-Commerce and Web Engineering||3|
|CSE 302||E-Commerce and Web Engineering Lab||1.5|
|CSE 304||Operating Systems Lab||1.5|
|CSE 315||Data Communication||3|
|CSE 351||Management Information System||3|
|CSE 403||Compiler Design||3|
|CSE 404||Compiler Design Lab||1.5|
|MTH 301||Statistics & Probability||2|
|Semester 3-1 (23 Credits)|
|Course Code||Course Title||Credit Hours|
|CSE 333||Microprocessors and Assembly Language||3|
|CSE 334||Microprocessors and Assembly Language Lab||1.5|
|CSE 339||Theory of Computation||2|
|CSE 401||Software Engineering||3|
|CSE 421||Computer Network||3|
|CSE 422||Computer Network Lab||1.5|
|CSE 435||Computer Interfacing||3|
|CSE 436||Computer Interfacing Lab||1.5|
|CSE 4**||Option Lab||1.5|
|Semester 3-2 (23 credits)|
|Course Code||Course Title||Credit Hours|
|CSE 405||Artificial Intelligence & Expert Systems||3|
|CSE 406||Artificial Intelligence & Expert Systems Lab||1.5|
|CSE 425||Digital Signal Processing||3|
|CSE 426||Digital Signal Processing Lab||1.5|
|CSE 431||Computer Graphics||3|
|CSE 432||Computer Graphics Lab||1.5|
|CSE 400||Project /Thesis||3|
|CVV 402||Comprehensive Viva Voce||2|
|CSE 4**||Option Lab||1.5|
Total Credit Hours Required for Degree
|Course Code||Course Title||Credit Hours|
|CSE 407||Simulation & Modeling||3|
|CSE 408||Simulation & Modeling Lab||1.5|
|CSE 411||VLSI Design||3|
|CSE 412||VLSI Design Lab||1.5|
|CSE 413||Information System Design||3|
|CSE 414||Information System Design Lab||1.5|
|CSE 419||Graph Theory||3|
|CSE 420||Graph Theory Lab||1.5|
|CSE 423||Computer System Performance Evaluation||3|
|CSE 424||Computer System Performance Evaluation Lab||1.5|
|CSE 437||Pattern Recognition||3|
|CSE 438||Pattern Recognition Lab||1.5|
|CSE 453||Digital Image Processing||3|
|CSE 454||Digital Image Processing Lab||1.5|
|CSE 455||Wireless and Sensor Networks||3|
|CSE 456||Wireless and Sensor Networks Lab||1.5|
|CSE 458||Bioinformatics Lab||1.5|
|CSE 461||Neural Networks||3|
|CSE 462||Neural Networks Lab||1.5|
|CSE 463||Machine Learning||3|
|CSE 464||Machine Learning Lab||1.5|
|CSE 465||Contemporary course on CSE||3|
|CSE 466||Contemporary course on CSE Lab||1.5|
HUM-103 Language Composition and Comprehension
This course purports to make the student well up in composition and comprehension of English language used in formal write ups like articles, essays and treatises. Here text will be given for comprehension, exercises of writing essays, paragraphs and reports will be done and construction of proper sentences expressing formal ideas will be taught. Sufficient exercises of translation and re-translations will be included.
- Exercise in Reading Comprehension – Tibbits
- Essential English Grammar – Ramon Murphy
- English Vocabulary in use – Stuart
- English Vocabulary in use – McCarthy
- Intermediate English Grammar – Ramon Murphy
- Paragraph in English – Tibbits
CSE-103 Computer Programming in C
Programming language: Basic concept; overview of programming languages, C-language: Preliminaries; Elements of C; program constructs; variables and data types in C; Input and output; character and formatted I/O; Arithmetic expressions and assignment statements; loops and nested loops; Decision making’ Arrays; Functions; Arguments and Local Variables; Calling functions and arrays; Recursion and recursive functions; structures within structure; Files; File functions for sequential and Random I/O. Pointers, Pointers and Structures; Pointers and functions; Pointer and arrays; Operations on pointers; Pointer and memory addresses; Operations on bits; Bit operation; Bit field; Advanced features; Standard and Library functions.
- The C Programming Language – Kernighn& Ritchie
- Teach Yourself C – H. Schieldt
- Programming with ANSI C – E. Balagurusamy
- The Complete Reference, Turbo C/C++ – H. Schieldt
- Programming with C, Schaum’s outline Series – Gotfreid
CSE-104 Computer Programming in C Lab
Laboratory works based on CSE 103.
PHY-101E Physics for Engineering
Properties of matter : Elasticity, Stress & Strain, Young’s Modulus, Surface Tension. Heat & Thermodynamics: Heat, Temperature, Zeroth Law of Thermodynamics, Thermal Equilibrium, Seebeck effect, Reversible & Irreversible Processes, First and Second law of Thermodynamics, Heat Engine, Carnot Cycle. Electromegnetism: Electric charge, Charge density, Coulomb’s and Ohm’s law, Electric field and electric potential, Electric dipole, Electric flux, Gauss’s law and its application, Capacitance, Magnetic field, Biot-Savert law, Ampere’s law and its application, Electromagnetic Induction, Faraday’s law, Lenz’s law, Self Inductance and Mutual Inductance. Optics: Nature and Propagation of light, Reflection and Refraction of light, Total Internal Reflection, Interference, Diffraction, Dispersion, Polarization. Modern Physics: Theory of Relativity, Length Contraction and Time Dilation, Mass-Energy Relation, Compton Effect, Photoelectric Effect, Quantum Theory, Atomic Structure, X-ray Diffraction, Atomic Spectra, Electron Orbital Wavelength, Bohr radius, Radioactivity, de Broglie theory, Nuclear Fission and Fusion.
- Modern Physics – Bernstein
- Concepts of Modern Physics – Beiser
- Heat & Thermodynamics – Brizlal
- University Physics with Modern Physics – Young
PHY 102 Physics Lab
Laboratory works based on PHY 101E.
MTH-101E Geometry, Differential and Integral Calculus
Geometry: Two dimensional geometry: Straight lines, pair of straight lines, Circle, Parabola, Ellipse and Hyperbola, Equation of General equation of Second Degree. Third Dimensional Geometry: Three dimensional Co-ordinates, Direction Cosines and Direction Ratios, Plane and Straight line. Differential Calculus: Real number system. Functions of single variables, its Graphs, Limit, Continuity and Differentiability. Successive Differentiation, Leibnitz’s theorem, Rolle’s theorem, Mean value theorem, Taylor’s theorem, Maclaurin’s theorem, Langrage’s and Cauchy’s forms of Remainder. Expansion of Function in Taylor’s and Maclaurin’s Series. Maximum and Minimum Values of Function. Evaluation of Indeterminate forms of limit, L’ Hospital’s Rule. Tangent and Normal. Functions of more than one variable, Limit, Continuity, Differentiability, Partial Derivatives, Euler’s Theorem. Jacobians. Integral Calculus: Indefinite Integrals and its definition. Methods of Integration (Integration by substitution, Integration by parts, Integration by successive reduction). Fundamental theorem of Integral calculus. Definite Integral and its properties. Definite Integral as the limit of a sum. Improper Integrals, Beta and Gamma Function, Its application in evaluating Integrals. Evaluation of Arc length, Areas, Surfaces of Revolution, Volumes of solids of Revolution, Multiple Integrals.
- Analytical Geometry of Conic Section – J.M. Kar.
- A Text Book on Co-ordinate Geometry – Rahman & Bhattacharjee; S. Chakrabarty, Gonith Prokashon.
- Calculus with Analytic Geometry – Thomas and Finne
- Calculus – Howard Anton; 10th Edition; John Willy and Sons
- Differential Calculus- C. Das & B. N. Mukharjee; 54th Edition; U. N. Dhur & Sons PTL
- Differential Calculus – C.Das & B. N. Mukharjee; 54th Edition; U. N. Dhur & Sons PTL
- Integral Calculus – C. Das & B. N. Mukharjee; 54th Edition; U. N. Dhur & Sons PTL
ECE 101 Basic Electrical Engineering
Fundamental electrical concepts, Kirchoff’s Laws, Equivalent resistance. Electrical circuits: Series circuits, parallel circuits, series-parallel networks. Network analysis: Source conversion, Star/Delta conversion, Branch-current method, Mesh analysis, Nodal analysis. Network theorems: Superposition theorem, Thevenin’s theorem, Norton’s theorem. Capacitors. Magnetic circuits, Inductors Sinosoidal alternating waveforms: Definitions, phase relations, Instantaneous value, Average value, Effective (rms)Value. Phasor algebra Series, parallel and series-parallel ac networks. Power: Apparent power, Reactive power, Power triangle, Power factor correction. Pulse waveforms and the R-C response. Three-phase system Transformers.
- Introductory Circuit Analysis- L. Boylestad
- Introduction to Electrical Engineering- P. Ward
- Electrical Technology (Volume 1)-L. Theraja, A.K.Theraja
- Alternating Current Circuits-M. Kerchner, G. F. Corcoran
- Electric Circuits – James W. Nilson
ECE 102 Basic Electrical Engineering Lab
Laboratory works based on ECE 101.
ECN 101 Principles of Economics
Introduction: The Nature, scope and methods of Economics, Economics and Engineering. Some Fundamental concepts commonly used in Economics. Micro Economics: The theory of demand and supply and their elasticity’s. Market price determination competition in theory and practice. Indifference curve technique. Marginal analysis. Factors of production and production function. Scale of production – Internal and external economies and diseconomies. The short run and the long run. Fixed cost and variable cost. Macro Economics: National income analysis. Inflation and its effects. Savings, Investments. The basis of trade and the terms of trade. Monetary policy, Fiscal policy, Trade policy with reference to Bangladesh. Planning in Bangladesh.
- Economics – Samuelson & Nordhaus
- Economics – Don Bush Fisher
ACN 201 Principles of Accounting
This course aims at developing basic concepts and principles of accounting. It will cover topics like working at journal entries, preparation of ledger, checking the accuracy through trial balance, and preparation of financial statements. Concepts and practices of cost accounting will be discussed by covering topics like job order and process costing, contract costing, differential costing and responsibility accounting. Contemporary practices of accounting principles will be discussed under the current legal framework.
- Accounting Principles – Kieso
- Financial & Managerial Accounting – Needles
CSE 200 Project Work
Project focusing on Object oriented programming approach and using standard algorithm is preferable. Every project should maintain a goal so that it can be used as a useful tool in the IT fields. Also innovative project ideas that require different types scripting/programming languages or programming tools can be accepted with respect to the consent of the corresponding project supervisor.
CSE-201 Discrete Mathematics
Mathematical Models and Reasoning: Propositions, Predicates and Quantifiers, Logical operators, Logical inference, Methods of proof. Sets: Set theory, Relations between sets, Operations on sets. Induction, The natural numbers, Set operations on å*. Binary Relations : Binary relations and Digraphs, Graph theory, Trees, Properties of relations, Composition of relations, Closure operations on relations, Order relations, Equivalence relations and partitions. Functions: Basic properties, Special classes of functions. Counting and Algorithm Analysis: Techniques, Asymptotic behavior of functions, Recurrence systems, Analysis of algorithms. Infinite sets: Finite and Infinite sets, Countable and uncountable sets, Comparison of cardinal numbers. Algebras: Structure, Varieties of algebras, Homomorphism, Congruence relations.
- Discrete Mathematics and its Applications- Kennth H. Rosen
- Discrete Mathematical Structures- Bernard Kolman, Robert C. Busby, Sharon Cutler Ross
- Concrete Mathematics- Ronald Ervin Knuth
CSE 203 Object Oriented Programming Language
Introduction to Java: History of Java, Java class Libraries, Introduction to java programming, and a simple program. Developing java Application: Introduction, Algorithms, Pseudo code, control Structure, The If/Else selection structure, the while Repetition structure, Assignment operators, Increment and decrement operators, Primitive data types, common Escape sequences, Logical operator. Control Structure: Introduction, for Structure, switch structure, Do while structure, Break and continue Structure. Methods: Introduction, Program module in Java, Math class methods, method definitions, java API packages, Automatic variables, Recursions, Method overloading, Method of the Applet class. Arrays: Introduction, Arrays, declaring and allocating arrays, passing arrays to methods, sorting arrays, searching arrays, multiple subscripted Arrays. Inheritance: Introduction, Super class, Subclass, Protected members, using constructor and Finalizes in subclasses, composition vs. Inheritance, Introduction to polymorphism, Dynamic method building, Final methods and classes, Abstract super classes and concrete classes, Exception Handling.
- Java, How to Program- H. M. Deitel & P. J. Deitel
- Core Java (Vol. 1 and 2)- Sun Press
- Beginning Java 2, Wrox – Ivor Horton
- Java 2 Complete Reference- H. Schieldt
CSE 204 Object Oriented Programming Language Lab
Laboratory works based on CSE 203.
CSE-205 Data Structures
Concepts and Examples: Introduction to Data structures. Elementary data structures: Arrays, records, pointer. Arrays: Type, memory representation and operations with arrays. Linked lists: Representation, Types and operations with linked lists. Stacks and Queues: Implementations, operations with stacks and queues. Graphs: Implementations, operations with graph. Trees: Representations, Types, operations with trees. Memory Management: Uniform size records, diverse size records. Sorting: Internal sorting, external sorting. Searching : List searching, tree searching. Hashing: Hashing functions, collision resolution.
- Fundamental of Data Structures – Horowitz & S. Sahni
- Data Structures – Reingold
- Data Structures, Schaum’s outline Series – Lipshultz
- Data Structures & Programming Design – Robert L. Kruse
CSE-206 Data Structures Lab
Laboratory works based on CSE 205.
ECE-201 Electronic Devices & Circuits
Introduction to semiconductors, Junction diode characteristics & diode applications, Bipolar Junction transistor characteristics, Transistor biasing, Small signal low frequency h-parameter model & hybrid -pi model, AC analysis of transistor, Frequency response of transistor, Operational amplifiers, Linear applications of operational amplifiers, DC performance of operational amplifiers, AC performance of operational amplifiers, Introduction to JFET, MOSFET, PMOS, NMOS & CMOS, Introduction to SCR, TRIAC, DIAC & UJT, Active filters Introduction to IC fabrication techniques & VLSI design.
- Electronic Devices & Circuits McGraw-Hill -Jacob Millman & Christos C. Halkias
- Electronics Devices And Circuits- Salivahanan, N. S. Kumar And A. Vallavaraj, Tata McGraw – Hill
- Electronics Fundamentals: Circuits, Devices, and Applications- Ronald J Tocci
ECE 202 Electronic Devices & Circuits Lab
Laboratory works based on ECE 201.
MTH-103E Linear Algebra, Vector Analysis and Complex Variables
Linear Algebra: Matrix, Types of Matrices, Matrix operations, Laws of matrix algebra, Invertible matrices, System of Linear equations (homogeneous and non-homogeneous) and their solution. Elementary row and column operations and Row reduced echelon matrices, Different types of matrices, Rank of matrices. Eigen values and Eigen vectors. Vector Analysis: Vector Algebra – Vectors in three dimensional space, Algebra of Vectors, Rectangular components, Addition and Scalar multiplication, Scalar and Vector product of two Vectors, Scalar and Vector triple product. Vector Calculus – Vector differentiation and Integration. Gradient, Divergence and Curl. Green’s theorem, Stoke’s theorem. Complex Variable: Limit, Continuity and differentiability of complex functions. Analytic function, Harmonic function, Cauchy-Rieman equation. Complex Integration. Cauchy’s integral theorem and Cauchy’s Integral formula. Lioville’s theorem. Taylor’s and Laurent’s theorems. Singularities. Residue, Cauchy’s Residue theorem. Contour Integration.
Book s Recommended:
- Scham’s Outline Series of the Theory and Problems on Linear Algebra – Seymour Lipschutz, 3rd ed., McGraw Hill Book
- Linear Algebra with Applications – R. Antone
- Scham’s Outline Series of the Theory and Problems on Vector Analysis – Murray R. Spiegel, SI(Metric ed.), McGraw Hill
- Functions of a Complex Variable – Dewan Abdul Quddus, Titash Publications.
IMG 201 Principles of Management
This course aims at providing students with concepts and tools of general management. The course covers concepts of planning, organizing, motivating and controlling, and its importance in attaining organizational objectives. Some current issues and trends in general management will also be discussed.
- Principles of Management – Mason Carpenter
- Principles of Management – Robert Kreitner
- Principles of Management : A Modern Approach – P.K.Saxena
- Principles of Management – P.C. Tripathi, P N Reddy, McGraw-Hill
Analysis of Algorithm: Asymptotic analysis: Recurrences, Substitution method, Recurrence tree method, Master method. Divide-and-Conquer: Binary search, Powering a number, Fibonacci numbers, Matrix Multiplication, Strassen’s Algorithm for Matrix Multiplication. Sorting: Insertion sort, Merge sort, Quick sort, Randomized quick sort, Decision tree, Counting sort, Radix sort. Order Statistics: Randomized divide and conquer, worst case linear time order statistics. Graph: Representation, Traversing a graph, Topological sorting, Connected Components. Dynamic Programming: Elements of DP (Optimal substructure, Overlapping subproblem), Longest Common Subsequence finding problem, Matrix Chain Multiplication. Greedy Method: Greedy choice property, elements of greedy strategy, Activity selector problem, Minimum spanning tree (Prims algorithm, Kruskal algorithm), Huffman coding. Shortest Path Algorithms: Dynamic and Greedy properties, Dijkstra’s algorithm with its correctness and analysis, Bellman-ford algorithm, All pair shortest path: Warshall’s algorithm, Johnson’s algorithm. Network flow: Maximum flow, Max-flow-min-cut, Bipartite matching. Backtracking/Branch-and-Bound: Permutation, Combination, 8-queen problem, 15-puzzle problem. Geometric algorithm: Segment-segment intersection, Convex-hull, Closest pair problem. And NP Completeness, NP hard and NP complete problems.
- Introduction to Algorithms- Thomas H. Cormen , Charles E. Leiserson.
- Algorithms –Robert Sedgewick and Kevin Wayne.
- Fundamental Algorithms- Donald E. Knuth,”Art of Computer Programming, Volume 1: Addison-
Wesley Professional; 3rd edition, 1997.
CSE-208 Algorithms Lab
Using different well known algorithms to solve the problem of Matrix-Chain Multiplication, Longest Common Subsequence, Huffman codes generation, Permutation, Combination, 8-queen problem, 15-puzzle, BFS, DFS, flood fill using DFS, Topological sorting, Strongly connected component, finding minimum spanning tree, finding shortest path (Dijkstra’s algorithm and Bellman-Ford’s algorithm), Flow networks and maximum bipartite matching, Finding the convex hull, Closest pair.
CSE-209 Numerical Methods
Errors and Accuracy. Iterative process: Solution of f(x)= 0, existence and convergence of a root, convergence of the iterative method, geometrical representation, Aitken’s D2– process of acceleration. System of Linear Equations. Solution of Non-Linear equations. Finite Differences and Interpolation. Finite Difference Interpolation. Numerical Differentiation. Numerical Integration. Differential Equations.
- Introductory methods of Numerical Analysis – S. S. Sastry
- Numerical Methods for Engineers –Steven C. Chapra
CSE-231 Digital Logic Design
Binary Logic. Logic Gates: IC digital logic families, positive and negative logic. Boolean Algebra. Simplification of Boolean Functions: Karnaugh map method, SOP and POS simplification, NAND, NOR, wired-AND, wired-OR implementation, nondegenerate forms, Don’t care conditions, Tabulation method – prime implicant chart. Combinational Logic: Arithmetic circuits – half and full adders and subtractors, multilevel NAND and NOR circuits, Ex-OR and Equivalence functions. Combinational Logic in MSI and LSI: Binary parallel adder, decimal and BCD adders, Comparators, Decoders and Encoders, Demultiplexors and Multiplexors. Sequential Logic. Registers and Counters. Synchronous Sequential Circuits. Asynchronous Sequential Circuits. Digital IC terminology, TTL logic family, TTL series characteristics, open-collector TTL, tristate TTL, ECL family, MOS digital ICs, MOSFET, CMOS characteristics, CMOS tristate logic, TTL-CMOS-TTL interfacing, memory terminology, general memory operation, semiconductor memory technologies, different types of ROMs, semiconductor RAMs, static and dynamic RAMs, magnetic bubble memory, CCD memory, FPGA Concept.
- Digital Logic & Computer Design-M. Morris Mano
- Digital Fundamentals- Floyd
- Modern Digital Electronics-R. P. Jain
- Digital Systems- R. J. Tocci
- Digital Electronics- Green
CSE-232 Digital Logic Design Lab
Laboratory works based on CSE 231.
CSE-321 Database Systems
Introduction: Purpose of Database Systems, Data Abstraction, Data Models, Instances and Schemes, Data Independence, Data Definition Language, Data Manipulation Language, Database Manager, Database administrator, Database Users, Overall System Structure, Advantages and Disadvantage of a Database Systems. Data Mining and analysis, Database Architecture, History of Database Systems Relationship Entity-Model: Entities and Entity Sets, Relationships and Relationship Sets, Attributes, Composite and Multivalued Attributes, Mapping Constraints, Keys, Entity-Relationship Diagram, Reducing of E-R Diagram to Tables, Generalization, Attribute Inheritance, Aggregation, Alternative E-R Notatios, Design of an E-R Database Scheme.
Relational Model: Structure of Relational Database, Fundamental Relational Algebra Operations, The Tuple Relational Calculus, The Domain Relational Calculus, Modifying the Database. Relational Commercial Language: SQL, Basic structure of SQL Queries, Query-by-Example, Quel., Nested Sub queries, Complex queries, Integrity Constraints, Authorization, Dynamic SQL, Recursive Queries. Relational Database Design: Pitfalls in Relational Database Design, Functional Dependency Theory, Normalization using Functional Dependencies, Normalization using Multivalued Dependencies, Normalization using join Dependencies, Database Design Process. File And System Structure: Overall System Structure, Physical Storage Media, File Organization, RAID, Organization of Records into Blocks, Sequential Files, Mapping Relational Data to Files, Data Dictionary Storage, Buffer Management. Indexing And Hashing: Basic Concepts, Ordered Indices, B+ -Tree Index Files, B-Tree Index Files, Static and Dynamic Hash Function, Comparison of Indexing and Hashing, Index Definition in SQL, Multiple Key Access.
Query Processing and Optimization: Query Interpretation, Equivalence of Expressions, Estimation of Query-Processing Cost, Estimation of Costs of Access Using Indices, Join Strategies, Join Strategies for parallel Processing, Structure of the query Optimizer, Transformation of Relational Expression. Concurrency Control: Schedules, Testing for Serializability, Lock-Based Protocols, Timestamp-Based Protocols, Validation Techniques, Multiple Granularity, Multiversion Schemes, Insert and Delete Operations, Deadlock Handling. Distributed Database: Structure of Distributed Databases, Trade-off in Distributing the Database, Design of Distributed Database, Transparancy and Autonomy, Distributed Query Processing, Recovery in Distributed Systems, Commit Protocols, Concurrency Control. Data Mining and Information Retrieval: Data analysis and OLAP, Data Warehouse, Data Mining, Relevance Ranking Using Terms, Relevance Ranking Using Hyperlink, Synonyms, Homonyms, Ontology, Indexing of Document, Measuring Retrieval Efficiencies, Information Retrieval and Structured Data.
- Database System Concepts – Abraham Silberschratz, Henry K. Korth, S. Sudarshan (5th edition)
- Fundamentals of Database Systems – Benjamin/Cummings, 1994
- Database Principles, Programming, Performance – Morgan Kaufmann 1994
- A First Course in Database Systems – Prentice Hall, 1997
- Database Management Systems, McGraw Hill, 1996
CSE-322 Database Systems Lab
Introduction: What is database, MySQL , Oracle , SQL, Datatypes, SQL / PLSQL, Oracle Software Installation, User Type, Creating User , Granting. Basic Parts of Speech in SQL: Creating Newspaper Table, Select Command (Where , order by), Creating View, Getting Text Information & Changing it, Concatenation, Cut & paste string(RPAD , LPAD , TRIM , LTRIM , RTRIM, LOWER , UPPER , INIT, LENGTH , SUBSTR , INSTR , SOUNDEX). Playing The Numbers: Addition , Subtraction , Multiplication , Division, NVL , ABS , Floor , MOD , Power , SQRT , EXR , LN , LOG , ROUND, AVG , MAX , MIN , COUNT , SUM, Distinct, SUBQUERY FOR MAX,MIN. Grouping things together: Group By , Having, Order By, Views Renaming Columns with Aliases. When one query depends upon another: Union, Intersect , Minus, Not in , Not Exists. Changing Data : INSERT,UPDATE,MERGE,DELETE, ROLLBACK , AUTOCOMMIT , COMMIT, SAVEPOINTS, MULTI TABLE INSERT, DELETE, UPDATE, MERGE. Creating And Altering tables & views: Altering table, Dropping table, Creating view, Creating a table from a table. By What Authority: Creating User, Granting User, Password Management.
An Introduction to PL/SQL: Implement few problems using PL/SQL (eg Prime Number, Factorial, Calculating Area of Circle, etc).An Introduction to Trigger and Procedure: Implement few problems using Trigger and Procedures. An Introduction to Indexing: Implement indexing using a large database and observe the difference of Indexed and Non-Indexed database.
CSE-331 Computer Architecture
Introduction to Computer Architecture: Overview and history; Cost factor; Performance metrics and evaluating computer designs. Instruction set design: Von Neumann machine cycle, Memory addressing, Classifying instruction set architectures, RISC versus CISC, Micro programmed vs. hardwired control unit. Memory System Design: Cache memory; Basic cache structure and design; Fully associative, direct, and set associative mapping; Analyzing cache effectiveness; Replacement policies; Writing to a cache; Multiple caches; Upgrading a cache; Main Memory; Virtual memory structure, and design; Paging; Replacement strategies. Pipelining: General considerations; Comparison of pipelined and nonpipelined computers; Instruction and arithmetic pipelines, Structural, Data and Branch hazards. Multiprocessors and Multi-core Computers: SISD, SIMD, and MIMD architectures; Centralized and distributed shared memory- architectures; Multi-core Processor architecture. Input/output Devices: Performance measure, Types of I/O device, Buses and interface to CPU, RAID. Pipelining: Basic pipelining, Pipeline Hazards. Parallel Processing.
- Computer Architecture and Organization- John P.Hayes, 3rd Edition, McGraw Hill
- Computer Organization and Design: The hardware / software interface- David A.Patterson and John L.Hennessy
MTH-203E Differential Equations, Laplace Transforms and Fourier Analysis
Differential Equation: Formation, Degree and Order of differential equation, Complete and Particular solution. Solution of ordinary differential equation of first order and first degree (special forms). Linear differential equation with constant coefficients. Homogeneous linear differential equation. Solution of equation by the method of Variation of parameters. Solution of linear differential equations in series by Frobenius method. Solution of Simultaneous equation of the form = = . Laplace Transforms: Definition, Laplace transforms of some elementary functions, sufficient conditions for existence of Laplace transforms, Inverse Laplace transforms, Laplace transforms of derivatives, Unit step function, Periodic function, Some special theorems on Laplace transforms, Partial fraction, Solution of differential equations by Laplace transforms, Evaluation of Improper Integrals. Fourier Analysis: Fourier series (Real and complex form). Finite transforms, Fourier Integrals, Fourier transforms and application in solving boundary value problems.
- Differential Equations – H. T. H. Piaggio; 1st Indian Edition, 1985, S. K. Jain for CBS Publishers
- A Text Book on Integral Calculus with Differential Equations – Mohammad, Bhattacharjee & Latif, 4th Edition, 2010; S. Chakravarty, Gonith Prokashon
- Schaum’s Outline Series of the Theory and Problems on Laplace Transforms – Murray R. Spiegel; Revised Edition, 2003; McGraw Hill Book Company
- Differential Equation – Md. Abu Eusuf; Latest Edition; Abdullah Al Mashud Publisher
CSE 300 Software Developments
Students will work in groups or individually to produce high quality software in different languages. Students will write structured programs and use proper documentation. Advanced programming techniques in Mobile Application.
- Android Application Development Cookbook- Wei-Meng Lee
- The Complete Android Guide- Kevin Purdy
CSE 301: E-Commerce and Web Engineering
E-Commerce Basics: E-Commerce Definition, Internet History and E-Commerce Development, Business-to-Business E-Commerce, Business-to-Consumer E-Commerce, E-Commerce Stages and Processes, E-Commerce Challenges, E-Commerce Opportunities.E-Commerce Options: Internet Access Requirements, Web Hosting Requirements, Entry-Level Options, Storefront and Template Services, E-Commerce Software Packages, E-Commerce Developers, E-Business Solutions.Marketing Issues: Online and Offline Market Research, Data Collection, Domain Names, Advertising Options, E-Mail Marketing, Search Engines, Web Site Monitoring, Incentives. Planning and Development: Web Site Goals, International Issues, Planning Stages, Resource Allocation, Content Development, Site Map Development, Web Site Design Principles, Web Site Design Tools, Web Page Programming Tools, Data-Processing Tools. E-Commerce Components: Navigation Aids, Web Site Search Tools, Databases, Forms, Shopping Carts, Checkout Procedures, Shipping Options. Payment Processing: Electronic Payment Issues, E-Cash, Credit Card Issues, Merchant Accounts, Online Payment Services, Transaction Processing, Taxation Issues, Mobile Commerce (M-Commerce). Security Issues: Security Issues and Threats, Security Procedures, Encryption, Digital Certificates, SSL and SET Technologies, Authentication and Identification, Security Providers, Privacy Policies, Legal Issues. Customer Service: Customer Service Issues, E-Mail Support , Telephone Support , Live Help Services, Customer Discussion Forums, Value-Added Options. ASP.NET programming model, Web development in Microsoft Visual Studio .NET, Anatomy of an ASP.NET page, ASP.NET core server controls, ADO.NET data providers, ADO.NET data containers, The data-binding model.
- E-Commerce, Jeffrey F., Rayport, Bernard J. Jaworsk , McGraw-Hill
- Understanding Electronic Commerce, David Kosiur , Microsoft Press.
- Introduction to E-Commerce, Jeffrey F. Rayport, et al. , McGraw-Hill
- E-Commerce Strategies, Charles Trepper
CSE 302: E-Commerce and Web Engineering Lab
Laboratory works based on CSE 301.
303 Operating Systems
Introduction: Operating Systems Concept, Computer System Structures, Operating System Structures, Operating System operations, Protection and Security, Special-Purpose Systems. Fundamentals of OS : OS services and components, multitasking, multiprogramming, time sharing, buffering, spooling Process Management: Process Concept, Process Scheduling, Process State, Process Management, Interprocess Communication, interaction between processes and OS, Communication in Client-Server Systems, Threading, Multithreading, Process Synchronization. Concurrency control: Concurrency and race conditions, mutual exclusion requirements, semaphores, monitors, classical IPC problem and solutions, Dead locks – characterization, detection, recovery, avoidance and prevention. Memory Management: Memory partitioning, Swapping, Paging, Segmentation, Virtual memory – Concepts, Overlays, Demand Paging, Performance of demand paging, Page replacement algorithm, Allocation algorithms. Storage Management: Principles of I/O hardware, Principles of I/O software, Secondary storage structure, Disk structure, Disk scheduling, Disk Management, Swap-space Management, Disk reliability, Stable storage implementation. File Concept: File support, Access methods, Allocation methods, Directory systems, File Protection, Free Space management Protection & Security : Goals of protection, Domain of protection, Access matrix, Implementation of access matrix, Revocation of access rights, The security problem, Authentication, One-time passwords, Program threats, System threats, Threat monitoring, Encryption, Computer-security classification. Distributed Systems: Types of Distributed Operating System, Communication Protocols, Distributed File Systems, Naming and Transparency, Remote File Access, Stateful Versus Stateless Service, File Replication. Case Studies: Study of a representative Operating Systems.
- Operating System Concepts – Silberschatz & Galvin Wiley 2000 (7th Edition)
- Operating Systems – Achyut S. Godbole Tata Mc Graw Hill (2nd Edition)
- Understanding Operating System – Flynn & Metioes Thomsan (4th Edition)
- Operating Systems Design & Implementation – Andrew Tanenbam, Albert S. Woodhull Pearson
- Modern Operating System – Andrew S. Tanenbaum
CSE-304 Operating Systems Lab
Thread programming: Creating thread and thread synchronization. Process Programming: The Process ID, Running a New Process, Terminating a Process, Waiting for Terminated Child Processes, Users and Groups, Sessions and Process Groups. Concurrent Programming: Using fork, exec for multi-task programs. File Operations: File sharing across processes, System lock table, Permission and file locking, Mapping Files into Memory, Synchronized, Synchronous, and Asynchronous Operations, I/O Schedulers and I/O Performance.
Communicating across processes: Using different signals, Pipes, Message queue, Semaphore, Semaphore arithmetic and Shared memory.
- The ‘C’ Odyssey UNIX-The Open, Boundless C – Meeta Gandhi, Tilak Shetty, Rajiv Shah.
- Beginning Linux Programming – Neil Matthew and Richard Stones
- Linux System Programming – Robert Love
CSE-315 Data Communication
Introduction to modulation techniques: Pulse modulation; pulse amplitude modulation, pulse width modulation and pulse position modulation. Pulse code modulation; quantization, Delta modulation. TDM, FDM, OOK, FSK, PSK, QPSK; Representation of noise; threshold effects in PCM and FM. Probability of error for pulse systems, concepts of channel coding and capacity. Asynchronous and synchronous communications. Hardware interfaces, multiplexers, concentrators and buffers. Communication medium, Fiber optics.
- Introduction to Data Communications-Eugene Blanchard
- Data Communication Principles – Ahmad, Aftab
- Data Communication & Networking– S.Bagad, I.A.Dhotre
- Data Communications and Networking- Behrouz A. Forouzan
CSE-351 Management Information Systems
Introduction to MIS: Management Information System Concept. Definitions, Role of MIS, Approaches of MIS development. MIS and Computer: Computer Hardware for Information System, Computer Software for Information System, Data Communication System, Database Management Technology, Client-Server Technology. Decision-Support System: Introduction, Evolution of DSS, Future development of DSS. Application of MIS: Applications in manufacturing Sector, Applications in service sector, Case Studies.
- Management Information Systems- James O’Brian , Tata MCGraw-Hill
- Management Information Systems-Post and Andersin, Tata Mcgraw-Hill
CSE-403 Compiler Design
Introduction to compilers: Introductory concepts, types of compilers, applications, phases of a compiler. Lexical analysis: Role of the lexical analyzer, input buffering, token specification, recognition of tokens, symbol tables. Parsing: Parser and its role, context free grammars, top-down parsing. Syntax-directed translation: Syntax-directed definitions, construction of syntax trees, top-down translation. Type checking: Type systems, type expressions, static and dynamic checking of types, error recovery. Run-time organization: Run-time storage organization, storage strategies. Intermediate code generation: Intermediate languages, declarations, assignment statements. Code optimization: Basic concepts of code optimization, principal sources of optimization. Code generation. Features of some common compilers: Characteristic features of C, Pascal and Fortran compilers.
- Compilers: Principles, Techniques, and Tools – Alfred V. Aho, Ravi Sethi, Jeffrey D. Ullman. Second Edition.
CSE-404 Compiler Design Lab
How to use scanner and parser generator tools (e.g., Flex, JFlex, CUP, Yacc, etc). For a given simple source language designing and implementing lexical analyzer, symbol tables, parser, intermediate code generator and code generator.
MTH-301 Statistics and Probability
Frequency distribution; mean, median, mode and other measures of central tendency, Standard deviation and other measures of dispersion, Moments, skewness and kurtosis, Elementary probability theory and discontinuous probability distribution, e.g. binomial, poison and negative binomial, Continuous probability distributions, e.g. normal and exponential, Characteristics of distributions, Hypothesis testing and regression analysis
- Introduction to M athematical Statistics – Hogg
- Probability and Statistics for Scientists and Engineers – Walpole
CSE-333 Microprocessors and Assembly Language
Introduction to different types of microprocessors, Microprocessor architecture, instruction set, interfacing, I/O operation, interrupt structure, DMA, Microprocessor interface ICs. Advanced microprocessor concept of microprocessor based system design. Machine and Assembly instruction types and their formats. Character representation instructions. Instruction execution. Machine language programming. Instruction sets and their implementations. The Assembly process. Addressing methods. Subroutines, macros and files. I/O programming interrupts and concurrent processes.
- Microprocessors & Interfacing- Douglas V. Hall
- Microprocessors – Harunur Rashid
- Microprocessor & Microcomputer Based System Design – Rafiquzzaman
- Microprocessor Systems: 8086/8088 Family – Y.Lin & G.A. Gibson
CSE-334 Microprocessors and Assembly Language Lab
Laboratory works based on CSE 333.
CSE-339 Theory of Computation
Finite Automata: Deterministic and nondeterministic finite automata and their equivalence. Equivalence with regular expressions. Closure properties. The pumping lemma and applications. Context-free Grammars: Definitions. Parse trees. The pumping lemma for CFLs and applications. Normal forms. General parsing. Sketch of equivalence with pushdown automata. Turing Machines: Designing simple TMs. Variations in the basic model(multi-tape, multi-head, nondeterminism). Church-Turing thesis and evidence to support it through the study of other models. Undecidability: The undecidability of the halting problem. Reductions to other problems. Reduction in general.
- Introduction to Languages and the Theory of Computation, 2nd Edition- C. Martin, McGraw Hill Publications, 1997.
CSE 401 Software Engineering
Concepts of software engineering: requirements definition, modularity, structured design, data specifications, functional specifications, verification, documentation, software maintenance. Software support tools. Software project organization, quality assurance, management and communication skills.
- Software Engineering: A Practitioner’s Approach, 6th Edition, Roger S. Pressman
- Software Engineering Concepts – Richard Fairley
- Software Engineering Environments – Robert N. Charette
CSE-421 Computer Network
Network architectures-layered architectures and ISO reference model: data link protocols, error control, HDLC, X.25, flow and congestion control, virtual terminal protocol, data security. Local area networks, satellite networks, packet radio networks. Introduction to ARPANET, SNA and DECNET. Topological design and queuing models for network and distributed computing systems.
- Computer Networks-A. S. Tanenbaum
- Introduction to Networking- Barry Nance
- Data Communications, Computer Networks & Open Systems- F. Halsall
- Data Communications and Networking-Behrouz A. Forouzan
CSE-422 Computer Network Lab
Laboratory works based on CSE 421.
CSE-435 Computer Interfacing
Interface components and their characteristics, microprocessor I/O. Disk, Drums, and Printers. Optical displays and sensors. High power interface devices, transducers, stepper motors and peripheral devices.
- Microprocessors & Interfacing-Douglas V. Hall
- Microprocessor & Microcomputer based System Design – Rafiquzzaman
- Microcomputer Interfacing-Artwick
- Microcomputer Interfacing-Ramesh Goanker
- Designing User Interfaces-James E. Powell
CSE-436 Computer Interfacing Lab
Laboratory works based on CSE 435.
CSE-405 Artificial Intelligence & Expert System
What is Artificial Intelligence: The AI problems, The underlying assumption, What is an AI technique. Problems, Problem spaces and Search: Defining the problem as a state space search, Production system, Problem characteristics. Heuristics Search Techniques: Generate and Test, Hill climbing, Best First Search, Problem Reduction, Constraint Satisfaction, Means-Ends Analysis. Knowledge Representation Issues: Representation and Mappings, Approaches to knowledge Representation, Issues in Knowledge representation. Using Predicate logic: Representing simple facts in logic, Representing Instance and Isa relationships, Computable functions and Predicates, Resolution. Representing Knowledge using Rules: Procedural versus Declarative Knowledge, Logic Programming, Forward versus Backward Reasoning, Matching. Game playing: Overview, The Mimimax Search Procedure, Adding Alpha-Beta cutoffs, Additional refinements, iterative Deepening, Planning: Overview, An example Domain: The Blocks World, Components of a planning system, Goal stack planning, Understanding: What is Understanding, What makes Understanding hard, Understanding as constraint satisfaction. natural Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Discourse and Pragmatic Processing. Expert systems: representing and using domain knowledge, Expert system shells explanation, Knowledge Acquisition. AI Programming Language: Prolog, LISP, Python.
- Artificial Intelligence: A Modern Approach-S. Russel and P. Norvig
- Introduction to Artificial Intelligence and Expert System-Dan W. Peterson
- Artificial Intelligence-E. Rich and K. Knight
- An Introduction to Neural Computing-C. F. Chabris and T. Jackson
- Artificial Intelligence using C – H. Schieldt
CSE-406 Artificial Intelligence & Expert System Lab
Students will have to understand the functionalities of intelligent agents and how the agents will solve general problems. Students have to use a high-level language (Python, Prolog, LISP) to solve the following problems:
Backtracking: State space, Constraint satisfaction, Branch and bound. Example: 8-queen, 8- puzzle, Crypt-arithmetic. BFS and production: Water jugs problem, The missionaries and cannibal problem. Heuristic and recursion: Tic-tac-toe, Simple bock world, Goal stack planning, The tower of Hanoi. Question answering: The monkey and bananas problem.
CSE-425 Digital Signal Processing
Introduction to digital signal processing (DSP): Discrete-time signals and systems, analog to digital conversion, impulse response, finite impulse response (FIR) and infinite impulse response (IIR) of discrete-time systems, difference equation, convolution, transient and steady state response. Discrete transformations: Discrete Fourier series, discrete-time Fourier series, discrete Fourier transform (DFT) and properties, fast Fourier transform (FFT), inverse fast Fourier transform, z-transformation – properties, transfer function, poles and zeros and inverse z-transform. Correlation: circular convolution, auto-correlation and cross correlation. Digital Filters: FIR filters- linear phase filters, specifications, design using window, optimal and frequency sampling methods; IIR filters- specifications, design using impulse invariant, bi-linear z-transformation, least-square methods and finite precision effects. Digital signal processor TMS family, Application of digital signal processing.
- Digital Signal Processing-John G. Proakis
- Signals and Systems-Simon Haykin and Barry Van Veen
- Digital Signal Processing-R. W. Schafer
- Digital Signal Processing-Ifeachor
- Introduction to DSP-Johnny R. Johnson
CSE-426 Digital Signal Processing Lab
Laboratory works based on CSE 425.
CSE-431 Computer Graphics
Introduction to Graphical data processing. Fundamentals of interactive graphics programming. Architecture of display devices and connectivity to a computer. Implementation of graphics concepts of two-dimensional and three-dimensional viewing, clipping and transformations. Hidden line algorithms. Raster graphics concepts: Architecture, algorithms and other image synthesis methods. Design of interactive graphic conversations.
- Principles of Interactive Computer Graphics –William M., Newman, McGraw-Hill, 2nd edition, 1978
- Computer Graphics: Principle and Practice in C-James D. Foley, Andries van Dam, Steven K. Feiner, John F. Hughes, Addison-Wesley, 2nd edition, 1995
CSE-432 Computer Graphics Lab
Laboratory works based on CSE 431.
CSE-400 Project / Thesis
Study of problems in the field of Computer Science and Engineering. This course will be initiated in the 3rd year or early in 4th year.
CSE-402 Comprehensive Viva Voce
CSE-407 Simulation and Modeling
Simulation methods, model building, random number generator, statistical analysis of results, validation and verification techniques, Digital simulation of continuous systems. Simulation and analytical methods, for analysis of computer systems and practical problems in business and practice. Introduction to the simulation packages.
- System Modeling and Simulation- V.P. Singh
- System Design, Modeling, and Simulation using- Claudius Ptolemaeus
CSE-408 Simulation and Modeling Lab
Laboratory works based on CSE 407.
CSE-411 VLSI Design
Design and analysis techniques for VLSI circuits. Design of reliable VLSI circuits, noise considerations, design and operation of large fan out and fan in circuits, clocking methodologies, techniques for data path and data control design. Simulation techniques. Parallel processing, special purpose architectures in VLSI. VLSI layouts partitioning and placement routing and wiring in VLSI. Reliability aspects of VLSI design.
- Basic VLSI Design-Douglas A Pucknell, Kamran Eshraghian
- VLSI Technology – S. M. Sze
- Introduction to VLSI Systems – C. A. Mead and L. A. Conway
CSE-412 VLSI Design Lab
Laboratory works based on CSE 411.
CSE-413 Information System Design
Information, general concepts of formal information systems, analysis of information requirements for modern organizations, modern data processing technology and its application, information systems structures, designing information outputs, classifying and coding data, physical storage media considerations, logical data, organization, systems analysis, general systems design, detail systems design. Project management and documentation. Group development of an information system project. Includes all phases of software life cycles from requirement analysis to the completion of a fully implemented system.
- Information Systems Analysis and Design – Phil Agre, Christine Borgman
- Analysis and Design of Information Systems-Langer, Arthur M.
CSE-414 Information System Design Lab
Laboratory works based on CSE 413.
CSE-419 Graph Theory
Introduction, Fundamental concepts, Trees, Spanning trees in graphs, Distance in graphs, Eulerian graphs, Digraphs, Matching and factors, Cuts and connectivity, k-connected graphs, Network flow problems, Graph coloring: vertex coloring and edge coloring, Line graphs, Hamiltonian cycles, Planar graphs, Perfect graphs.
- Graph Theory and Its Applications – Jonathan L. Gross, Jay Yellen
- A Textbook of Graph Theory – R. Balakrishnan, K. Ranganathan
CSE-420 Graph Theory Lab
Laboratory works based on CSE 420.
CSE-423 Computer System Performance Evaluations
Review of system analysis, approaches to system development, feasibility assessment, hardware and software acquisition. Procurement, workload characterization, the representation of measurement data, instrumentation: software monitors, hardware monitors, capacity planning, bottleneck detection, system and program tuning, simulation and analytical models and their application, case studies.
- Computer Systems Performance Evaluation and Prediction– Paul J. Fortier and Howard E. Michel
- The Art of Computer Systems Performance Analysis- Jain
CSE-424 Computer System Performance Evaluation Lab
Laboratory based on CSE 423.
CSE-437 Pattern Recognition
Introduction to pattern recognition: features, classifications, learning. Statistical methods, structural methods and hybrid method. Applications to speech recognition, remote sensing and biomedical area, Learning algorithms. Syntactic approach: Introduction to pattern grammars and languages. parsing techniques. Pattern recognition in computer aided design.
- Pattern Recognition- K. Koutroumbas
- Pattern Recognition and Machine Learning- Christopher M. Bishop
- Pattern Recognition for Neural Networks- Brian Ripley
CSE-438 Pattern Recognition Lab
Laboratory works based on CSE 437.
CSE-453 Digital Image Processing
Image Processing: Image Fundamentals, Image Enhancement: Background, Enhancement by Point-Processing, Spatial Filtering, Enhancement in Frequency Domain, Color Image Processing. Image Restoration: Degradation Model, Diagonalization of Circulant and Block-Circulant Matrices, Algebraic Approach to Restoration, Inverse Filtering, Geometric Transformation. Image Segmentation: Detection of Discontinuities, Edge Linking and Boundary Detection, Thresholding, Region-Oriented Segmentation, The use of Motion in Segmentation. Image Compression.
- Digital Image Processing-Rafael C. Gonzalez and Richard E. Woods, Pearson Education Asia.
- Non-Linear Digital Filter : Principles and Applications –I. Pitas and A. N. Venetsanopoulos, Kluwer Academic Publications.
CSE-454 Digital Image Processing Lab
Laboratory works based on CSE 453.
CSE-455 Wireless and Sensor Networks
Introduction: applications; Localization and tracking: tracking multiple objects; Medium Access Control: S-MAC, IEEE 802.15.4 and ZigBee; Geographic and energy-aware routing; Attribute-Based Routing: directed diffusion, rumor routing, geographic hash tables; Infrastructure establishment: topology control, clustering, time synchronization; Sensor tasking and control: task-driven sensing, information-based sensor tasking, joint routing and information aggregation; Sensor network databases: challenges, querying the physical environment, in-network aggregation, data indices and range queries, distributed hierarchical aggregation; Sensor network platforms and tools: sensor node hardware, sensor network programming challenges; Other state-of-the-art related topics.
- Wireless Sensor Networks – C. S. Raghavendra, Krishna M. Sivalingam and TaiebZnati
- Wireless Sensor Networks: An Information Processing Approach (The Morgan Kaufmann Series in Networking) – Feng Zhao,Leonidas Guibas
CSE-456 Wireless and Sensor Networks Lab
Laboratory works based on CSE 455.
Cell concept: Structural organization of plant and animal cells, nucleus, cell membrane and cell wall. Cell division: Introducing chromosome, Mitosis, Meiosis and production of haploid/diploid cell. Nucleic acids: Structure and properties of different forms of DNA and RNA; DNA replication. Proteins: Structure and classification, Central dogma of molecular biology. Genetic code: A brief account. Genetics: Mendel’s laws of inheritance, Organization of genetic material of prokaryotes and eukaryotes, C-Value paradox, repetitive DNA, structure of chromatin – euchromatin and heterochromatin, chromosome organization and banding patterns, structure of gene – intron, exon and their relationships, overlapping gene, regulatory sequence (lac operon), Molecular mechanism of general recombination, gene conversion, Evolution and types of mutation, molecular mechanisms of mutation, site-directed mutagenesis, transposons in mutation. Introduction to Bioinformatics: Definition and History of Bioinformatics, Human Genome Project, Internet and Bioinformatics, Applications of Bioinformatics Sequence alignment: Dynamic programming. Global versus local. Scoring matrices. The Blast family of programs. Significance of alignments, Aligning more than two sequences. Genomes alignment. Structure-based alignment. Hidden Markov Models in Bioinformatics: Definition and applications in Bioinformatics. Examples of the Viterbi, the Forward and the Backward algorithms. Parameter estimation for HMMs. Trees: The Phylogeny problem. Distance methods, parsimony, bootstrap. Stationary Markov processes. Rate matrices. Maximum likelihood. Felsenstein’s post-order traversal. Finding regulatory elements: Finding regulatory elements in aligned and unaligned sequences. Gibbs sampling. Introduction to microarray data analysis: Steady state and time series microarray data. From microarray data to biological networks. Identifying regulatory elements using microarray data. Pi calculus: Description of biological networks; stochastic Pi calculus, Gillespie algorithm.
- Introduction to Bioinformatics Algorithms –Jones and Pavel A. Pevzner
- Introduction to Bioinformatics – Stephen A. Krawetz, David D. Womble
- Introduction to Bioinformatics – Arthur M. Lesk
CSE-458 Bio-Informatics Lab
Laboratory works based on CSE-457.
CSE-461 Neural Networks
Fundamentals of Neural Networks; Back propagation and related training algorithms; Hebbian learning; Cohonen-Grossberg learning; The BAM and the Hopfield Memory; Simulated Annealing; Different types of Neural Networks: Counter propagation, Probabilistic, Radial Basis Function, Generalized Regression, etc; Adaptive Resonance Theory; Dynamic Systems and neural Control; The Boltzmann Machine; Self-organizing Maps; Spatiotemporal Pattern Classification, The Neocognition; Practical Aspects of Neural Networks.
- An Introduction to Neural Networks – Prof. Leslie Smith
- Fundamentals of Artificial Neural Networks – Mohamad H. Hassoun
CSE-462 Neural Networks Lab
Laboratory works based on CSE 461.
CSE-463 Machine Learning
Introduction: Definition of learning systems. Goals and applications of machine learning. Aspects of developing a learning system- training data, concept representation, function approximation. Inductive Classification: The concept learning task. Concept learning as search through a hypothesis space. General-to-specific ordering of hypotheses. Finding maximally specific hypotheses. Version spaces and the candidate elimination algorithm. Learning conjunctive concepts. The importance of inductive bias. Decision Tree Learning: Representing concepts as decision trees. Recursive induction of decision trees. Picking the best splitting attribute: entropy and information gain. Searching for simple trees and computational complexity. Occam’s razor. Overfitting, noisy data, and pruning. Experimental Evaluation of Learning Algorithms: Measuring the accuracy of learned hypotheses. Comparing learning algorithms- cross-validation, learning curves, and statistical hypothesis testing. Computational Learning Theory: Models of learnability- learning in the limit; probably approximately correct (PAC) learning. Sample complexity- quantifying the number of examples needed to PAC learn. Computational complexity of training. Sample complexity for finite hypothesis spaces. PAC results for learning conjunctions, kDNF, and kCNF. Sample complexity for infinite hypothesis spaces, Vapnik-Chervonenkis dimension. Rule Learning, Propositional and First-Order: Translating decision trees into rules. Heuristic rule induction using separate and conquer and information gain. First-order Horn-clause induction (Inductive Logic Programming) and Foil. Learning recursive rules. Inverse resolution, Golem, and Progol. Artificial Neural Networks: Neurons and biological motivation. Linear threshold units. Perceptrons: representational limitation and gradient descent training. Multilayer networks and backpropagation. Hidden layers and constructing intermediate, distributed representations. Overfitting, learning network structure, recurrent networks. Support Vector Machines: Maximum margin linear separators. Quadractic programming solution to finding maximum margin separators. Kernels for learning non-linear functions. Bayesian Learning: Probability theory and Bayes rule. Naive Bayes learning algorithm. Parameter smoothing. Generative vs. discriminative training. Logisitic regression. Bayes nets and Markov nets for representing dependencies. Instance-Based Learning: Constructing explicit generalizations versus comparing to past specific examples. k-Nearest-neighbor algorithm. Case-based learning. Text Classification: Bag of words representation. Vector space model and cosine similarity. Relevance feedback and Rocchio algorithm. Versions of nearest neighbor and Naive Bayes for text. Clustering and Unsupervised Learning: Learning from unclassified data. Clustering. Hierarchical Aglomerative Clustering. k-means partitional clustering. Expectation maximization (EM) for soft clustering. Semi-supervised learning with EM using labeled and unlabled data.
- Artificial Intelligence: a modern approach (2nd edition), Russell, S. and P. Norvig, Prentice Hall, 2003
- Introduction to Machine Learning – Ethem ALPAYDIN
- Machine Learning – Tom Mitchell, McGraw Hill
- Introduction to machine learning (2nd edition), Alpaydin, Ethem, MIT Press, 2010
- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, NelloCristianini and John Shawe-Taylor, Cambridge University Press
CSE-464 Machine Learning Lab
Students should learn the methods for extracting rules or learning from data, and get the necessary mathematical background to understand how the methods work and how to get the best performance from them. To achieve these goals student should learn the following algorithms in the lab: K Nearest Neighbor Classifier, Decision Trees, Model Selection and Empirical Methodologies, Linear Classifiers: Perception and SVM, Naive Bayes Classifier, Basics of Clustering Analysis, K-mean Clustering Algorithm, Hierarchical Clustering Algorithm. Upon completion of the course, the student should be able to perform the followings: a. Evaluate whether a learning system is required to address a particular problem. b. Understand how to use data for learning, model selection, and testing to achieve the goals. c. Understand generally the relationship between model complexity and model performance, and be able to use this to design a strategy to improve an existing system. d. Understand the advantages and disadvantages of the learning systems studied in the course, and decide which learning system is appropriate for a particular application. e. Make a naive Bayes classifier and interpret the results as probabilities. f. Be able to apply clustering algorithms to simple data sets for clustering analysis.
CSE-465 Contemporary course on CSE
CSE-466 Contemporary course on CSE Lab
Laboratory works based on CSE 465