CSE (Evening – Fall 2015 / Spring 2016 / Fall 2016)
Semester 11 (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 12 (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 21 (22.5 Credits) 
Course Code  Course Title  Credit Hours 
CSE 207  Algorithms  3 
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 22 (23.5 Credits) 
Course Code  Course Title  Credit Hours 
CSE 300  Software Development  2 
CSE 301  ECommerce and Web Engineering  3 
CSE 302  ECommerce and Web Engineering Lab  1.5 
CSE303  Operating Systems  3 
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 31 (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  3 
CSE 4**  Option Lab  1.5 
Semester 32 (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  3 
CSE 4**  Option Lab  1.5 
Total Credit Hours Required for Degree

139.00 
Optional Courses:
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 457  Bioinformatics  3 
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 
Detailed Syllabus
HUM103 Language Composition and Comprehension
3 Credits
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 retranslations will be included.
Books Recommended:
 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
CSE103 Computer Programming in C
3 Credits
Programming language: Basic concept; overview of programming languages, Clanguage: 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.
Books Recommended:
 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
CSE104 Computer Programming in C Lab
1.5 Credits
Laboratory works based on CSE 103.
PHY101E Physics for Engineering
3 Credits
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, BiotSavert 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, MassEnergy Relation, Compton Effect, Photoelectric Effect, Quantum Theory, Atomic Structure, Xray Diffraction, Atomic Spectra, Electron Orbital Wavelength, Bohr radius, Radioactivity, de Broglie theory, Nuclear Fission and Fusion.
Books Recommended:
 Modern Physics – Bernstein
 Concepts of Modern Physics – Beiser
 Heat & Thermodynamics – Brizlal
 University Physics with Modern Physics – Young
PHY 102 Physics Lab
1.5 Credits
Laboratory works based on PHY 101E.
MTH101E Geometry, Differential and Integral Calculus
3 Credits
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 Coordinates, 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.
Books Recommended:
 Analytical Geometry of Conic Section – J.M. Kar.
 A Text Book on Coordinate Geometry – Rahman & Bhattacharjee; S. Chakrabarty, Gonith Prokashon.
 Calculus with Analytic Geometry – Thomas and Finne
 Calculus – Howard Anton; 10^{th} Edition; John Willy and Sons
 Differential Calculus C. Das & B. N. Mukharjee; 54^{th} Edition; U. N. Dhur & Sons PTL
 Differential Calculus – C.Das & B. N. Mukharjee; 54^{th} Edition; U. N. Dhur & Sons PTL
 Integral Calculus – C. Das & B. N. Mukharjee; 54^{th} Edition; U. N. Dhur & Sons PTL
ECE 101 Basic Electrical Engineering
3 Credits
Fundamental electrical concepts, Kirchoff’s Laws, Equivalent resistance. Electrical circuits: Series circuits, parallel circuits, seriesparallel networks. Network analysis: Source conversion, Star/Delta conversion, Branchcurrent 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 seriesparallel ac networks. Power: Apparent power, Reactive power, Power triangle, Power factor correction. Pulse waveforms and the RC response. Threephase system Transformers.
Books Recommended:
 Introductory Circuit Analysis L. Boylestad
 Introduction to Electrical Engineering P. Ward
 Electrical Technology (Volume 1)L. Theraja, A.K.Theraja
 Alternating Current CircuitsM. Kerchner, G. F. Corcoran
 Electric Circuits – James W. Nilson
ECE 102 Basic Electrical Engineering Lab
1.5 Credits
Laboratory works based on ECE 101.
ECN 101 Principles of Economics
2 Credits
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.
Books Recommended:
 Economics – Samuelson & Nordhaus
 Economics – Don Bush Fisher
ACN 201 Principles of Accounting
2 Credits
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.
Books Recommended:
 Accounting Principles – Kieso
 Financial & Managerial Accounting – Needles
CSE 200 Project Work
2 Credits
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.
CSE201 Discrete Mathematics
3 Credits
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.
Books Recommended:
 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
3 Credits
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.
Books Recommended:
 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
1.5 Credits
Laboratory works based on CSE 203.
CSE205 Data Structures
3 Credits
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.
Books Recommended:
 Fundamental of Data Structures – Horowitz & S. Sahni
 Data Structures – Reingold
 Data Structures, Schaum’s outline Series – Lipshultz
 Data Structures & Programming Design – Robert L. Kruse
CSE206 Data Structures Lab
1.5 Credits
Laboratory works based on CSE 205.
ECE201 Electronic Devices & Circuits
3 Credits
Introduction to semiconductors, Junction diode characteristics & diode applications, Bipolar Junction transistor characteristics, Transistor biasing, Small signal low frequency hparameter 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.
Books Reccommended:
 Electronic Devices & Circuits McGrawHill 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
1.5 Credits
Laboratory works based on ECE 201.
MTH103E Linear Algebra, Vector Analysis and Complex Variables
3 Credits
Linear Algebra: Matrix, Types of Matrices, Matrix operations, Laws of matrix algebra, Invertible matrices, System of Linear equations (homogeneous and nonhomogeneous) 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, CauchyRieman 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, 3^{rd} 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.
Books Reccommended:
 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, McGrawHill
CSE207 Algorithms
3 Credits
Analysis of Algorithm: Asymptotic analysis: Recurrences, Substitution method, Recurrence tree method, Master method. DivideandConquer: 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, Bellmanford algorithm, All pair shortest path: Warshall’s algorithm, Johnson’s algorithm. Network flow: Maximum flow, Maxflowmincut, Bipartite matching. Backtracking/BranchandBound: Permutation, Combination, 8queen problem, 15puzzle problem. Geometric algorithm: Segmentsegment intersection, Convexhull, Closest pair problem. And NP Completeness, NP hard and NP complete problems.
Books Recommended:
 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.
CSE208 Algorithms Lab
1.5 Credits
Using different well known algorithms to solve the problem of MatrixChain Multiplication, Longest Common Subsequence, Huffman codes generation, Permutation, Combination, 8queen problem, 15puzzle, BFS, DFS, flood fill using DFS, Topological sorting, Strongly connected component, finding minimum spanning tree, finding shortest path (Dijkstra’s algorithm and BellmanFord’s algorithm), Flow networks and maximum bipartite matching, Finding the convex hull, Closest pair.
CSE209 Numerical Methods
3 Credits
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 D^{2}– process of acceleration. System of Linear Equations. Solution of NonLinear equations. Finite Differences and Interpolation. Finite Difference Interpolation. Numerical Differentiation. Numerical Integration. Differential Equations.
Books Recommended:
 Introductory methods of Numerical Analysis – S. S. Sastry
 Numerical Methods for Engineers –Steven C. Chapra
CSE231 Digital Logic Design
3 Credits
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, wiredAND, wiredOR 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, ExOR 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, opencollector TTL, tristate TTL, ECL family, MOS digital ICs, MOSFET, CMOS characteristics, CMOS tristate logic, TTLCMOSTTL 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.
Books Recommended:
 Digital Logic & Computer DesignM. Morris Mano
 Digital Fundamentals Floyd
 Modern Digital ElectronicsR. P. Jain
 Digital Systems R. J. Tocci
 Digital Electronics Green
CSE232 Digital Logic Design Lab
1.5 Credits
Laboratory works based on CSE 231.
CSE321 Database Systems
3 Credits
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 EntityModel: Entities and Entity Sets, Relationships and Relationship Sets, Attributes, Composite and Multivalued Attributes, Mapping Constraints, Keys, EntityRelationship Diagram, Reducing of ER Diagram to Tables, Generalization, Attribute Inheritance, Aggregation, Alternative ER Notatios, Design of an ER 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, QuerybyExample, 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, BTree 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 QueryProcessing 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, LockBased Protocols, TimestampBased Protocols, Validation Techniques, Multiple Granularity, Multiversion Schemes, Insert and Delete Operations, Deadlock Handling. Distributed Database: Structure of Distributed Databases, Tradeoff 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.
Books Recommended:
 Database System Concepts – Abraham Silberschratz, Henry K. Korth, S. Sudarshan (5^{th} 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
CSE322 Database Systems Lab
1.5 Credits
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 NonIndexed database.
CSE331 Computer Architecture
3 Credits
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 Multicore Computers: SISD, SIMD, and MIMD architectures; Centralized and distributed shared memory architectures; Multicore 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.
Books Recommended:
 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
MTH203E Differential Equations, Laplace Transforms and Fourier Analysis
3 Credits
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.
Books Recommended:
 Differential Equations – H. T. H. Piaggio; 1^{st} Indian Edition, 1985, S. K. Jain for CBS Publishers
 A Text Book on Integral Calculus with Differential Equations – Mohammad, Bhattacharjee & Latif, 4^{th} 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
1.5 Credits
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.
Books Recommended:
 Android Application Development Cookbook WeiMeng Lee
 The Complete Android Guide Kevin Purdy
CSE 301: ECommerce and Web Engineering
3 Credits
ECommerce Basics: ECommerce Definition, Internet History and ECommerce Development, BusinesstoBusiness ECommerce, BusinesstoConsumer ECommerce, ECommerce Stages and Processes, ECommerce Challenges, ECommerce Opportunities.ECommerce Options: Internet Access Requirements, Web Hosting Requirements, EntryLevel Options, Storefront and Template Services, ECommerce Software Packages, ECommerce Developers, EBusiness Solutions.Marketing Issues: Online and Offline Market Research, Data Collection, Domain Names, Advertising Options, EMail 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, DataProcessing Tools. ECommerce Components: Navigation Aids, Web Site Search Tools, Databases, Forms, Shopping Carts, Checkout Procedures, Shipping Options. Payment Processing: Electronic Payment Issues, ECash, Credit Card Issues, Merchant Accounts, Online Payment Services, Transaction Processing, Taxation Issues, Mobile Commerce (MCommerce). 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, EMail Support , Telephone Support , Live Help Services, Customer Discussion Forums, ValueAdded 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 databinding model.
Books Recommended
 ECommerce, Jeffrey F., Rayport, Bernard J. Jaworsk , McGrawHill
 Understanding Electronic Commerce, David Kosiur , Microsoft Press.
 Introduction to ECommerce, Jeffrey F. Rayport, et al. , McGrawHill
 ECommerce Strategies, Charles Trepper
CSE 302: ECommerce and Web Engineering Lab
1.5 Credits
Laboratory works based on CSE 301.
303 Operating Systems
3 Credits
Introduction: Operating Systems Concept, Computer System Structures, Operating System Structures, Operating System operations, Protection and Security, SpecialPurpose 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 ClientServer 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, Swapspace 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, Onetime passwords, Program threats, System threats, Threat monitoring, Encryption, Computersecurity 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.
Books Recommended:
 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
CSE304 Operating Systems Lab
1.5 Credits
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 multitask 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.
Books Recommended:
 The ‘C’ Odyssey UNIXThe Open, Boundless C – Meeta Gandhi, Tilak Shetty, Rajiv Shah.
 Beginning Linux Programming – Neil Matthew and Richard Stones
 Linux System Programming – Robert Love
CSE315 Data Communication
3 Credits
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.
Books Recommended:
 Introduction to Data CommunicationsEugene Blanchard
 Data Communication Principles – Ahmad, Aftab
 Data Communication & Networking– S.Bagad, I.A.Dhotre
 Data Communications and Networking Behrouz A. Forouzan
CSE351 Management Information Systems
3 Credits
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, ClientServer Technology. DecisionSupport System: Introduction, Evolution of DSS, Future development of DSS. Application of MIS: Applications in manufacturing Sector, Applications in service sector, Case Studies.
Books Recommended:
 Management Information Systems James O’Brian , Tata MCGrawHill
 Management Information SystemsPost and Andersin, Tata McgrawHill
CSE403 Compiler Design
3 Credits
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, topdown parsing. Syntaxdirected translation: Syntaxdirected definitions, construction of syntax trees, topdown translation. Type checking: Type systems, type expressions, static and dynamic checking of types, error recovery. Runtime organization: Runtime 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.
Books Recommended:
 Compilers: Principles, Techniques, and Tools – Alfred V. Aho, Ravi Sethi, Jeffrey D. Ullman. Second Edition.
CSE404 Compiler Design Lab
1.5 Credits
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.
MTH301 Statistics and Probability
2 Credits
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
Books Recommended:
 Introduction to M athematical Statistics – Hogg
 Probability and Statistics for Scientists and Engineers – Walpole
CSE333 Microprocessors and Assembly Language
3 Credits
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.
Books Recommended:
 Microprocessors & Interfacing Douglas V. Hall
 Microprocessors – Harunur Rashid
 Microprocessor & Microcomputer Based System Design – Rafiquzzaman
 Microprocessor Systems: 8086/8088 Family – Y.Lin & G.A. Gibson
CSE334 Microprocessors and Assembly Language Lab
1.5 Credits
Laboratory works based on CSE 333.
CSE339 Theory of Computation
2 Credits
Finite Automata: Deterministic and nondeterministic finite automata and their equivalence. Equivalence with regular expressions. Closure properties. The pumping lemma and applications. Contextfree 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(multitape, multihead, nondeterminism). ChurchTuring 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.
Books Recommended:
 Introduction to Languages and the Theory of Computation, 2^{nd} Edition C. Martin, McGraw Hill Publications, 1997.
CSE 401 Software Engineering
3 credits
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.
Books Reccommended:
 Software Engineering: A Practitioner’s Approach, 6^{th} Edition, Roger S. Pressman
 Software Engineering Concepts – Richard Fairley
 Software Engineering Environments – Robert N. Charette
CSE421 Computer Network
3 Credits
Network architectureslayered 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.
Books Recommended:
 Computer NetworksA. S. Tanenbaum
 Introduction to Networking Barry Nance
 Data Communications, Computer Networks & Open Systems F. Halsall
 TCP/IPSydniFeit
 Data Communications and NetworkingBehrouz A. Forouzan
CSE422 Computer Network Lab
1.5 Credits
Laboratory works based on CSE 421.
CSE435 Computer Interfacing
3 Credits
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.
Books Recommended:
 Microprocessors & InterfacingDouglas V. Hall
 Microprocessor & Microcomputer based System Design – Rafiquzzaman
 Microcomputer InterfacingArtwick
 Microcomputer InterfacingRamesh Goanker
 Designing User InterfacesJames E. Powell
CSE436 Computer Interfacing Lab
1.5 Credits
Laboratory works based on CSE 435.
CSE405 Artificial Intelligence & Expert System
3 Credits
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, MeansEnds 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 AlphaBeta 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.
Books Recommended:
 Artificial Intelligence: A Modern ApproachS. Russel and P. Norvig
 Introduction to Artificial Intelligence and Expert SystemDan W. Peterson
 Artificial IntelligenceE. Rich and K. Knight
 An Introduction to Neural ComputingC. F. Chabris and T. Jackson
 Artificial Intelligence using C – H. Schieldt
CSE406 Artificial Intelligence & Expert System Lab
1.5 Credits
Students will have to understand the functionalities of intelligent agents and how the agents will solve general problems. Students have to use a highlevel language (Python, Prolog, LISP) to solve the following problems:
Backtracking: State space, Constraint satisfaction, Branch and bound. Example: 8queen, 8 puzzle, Cryptarithmetic. BFS and production: Water jugs problem, The missionaries and cannibal problem. Heuristic and recursion: Tictactoe, Simple bock world, Goal stack planning, The tower of Hanoi. Question answering: The monkey and bananas problem.
CSE425 Digital Signal Processing
3 Credits
Introduction to digital signal processing (DSP): Discretetime signals and systems, analog to digital conversion, impulse response, finite impulse response (FIR) and infinite impulse response (IIR) of discretetime systems, difference equation, convolution, transient and steady state response. Discrete transformations: Discrete Fourier series, discretetime Fourier series, discrete Fourier transform (DFT) and properties, fast Fourier transform (FFT), inverse fast Fourier transform, ztransformation – properties, transfer function, poles and zeros and inverse ztransform. Correlation: circular convolution, autocorrelation 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, bilinear ztransformation, leastsquare methods and finite precision effects. Digital signal processor TMS family, Application of digital signal processing.
Books Recommended:
 Digital Signal ProcessingJohn G. Proakis
 Signals and SystemsSimon Haykin and Barry Van Veen
 Digital Signal ProcessingR. W. Schafer
 Digital Signal ProcessingIfeachor
 Introduction to DSPJohnny R. Johnson
CSE426 Digital Signal Processing Lab
1.5 Credits
Laboratory works based on CSE 425.
CSE431 Computer Graphics
3 Credits
Introduction to Graphical data processing. Fundamentals of interactive graphics programming. Architecture of display devices and connectivity to a computer. Implementation of graphics concepts of twodimensional and threedimensional viewing, clipping and transformations. Hidden line algorithms. Raster graphics concepts: Architecture, algorithms and other image synthesis methods. Design of interactive graphic conversations.
Books Recommended:
 Principles of Interactive Computer Graphics –William M., Newman, McGrawHill, 2nd edition, 1978
 Computer Graphics: Principle and Practice in CJames D. Foley, Andries van Dam, Steven K. Feiner, John F. Hughes, AddisonWesley, 2nd edition, 1995
CSE432 Computer Graphics Lab
1.5 Credits
Laboratory works based on CSE 431.
CSE400 Project / Thesis
3 Credits
Study of problems in the field of Computer Science and Engineering. This course will be initiated in the 3^{rd} year or early in 4^{th} year.
CSE402 Comprehensive Viva Voce
2 Credits
OPTIONAL COURSES
CSE407 Simulation and Modeling
3 Credits
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.
Books Recommended:
 System Modeling and Simulation V.P. Singh
 System Design, Modeling, and Simulation using Claudius Ptolemaeus
CSE408 Simulation and Modeling Lab
1.5 Credits
Laboratory works based on CSE 407.
CSE411 VLSI Design
3 Credits
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.
Books Recommended:
 Basic VLSI DesignDouglas A Pucknell, Kamran Eshraghian
 VLSI Technology – S. M. Sze
 Introduction to VLSI Systems – C. A. Mead and L. A. Conway
CSE412 VLSI Design Lab
1.5 Credits
Laboratory works based on CSE 411.
CSE413 Information System Design
3 Credits
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.
Books Recommended:
 Information Systems Analysis and Design – Phil Agre, Christine Borgman
 Analysis and Design of Information SystemsLanger, Arthur M.
CSE414 Information System Design Lab
1.5 Credits
Laboratory works based on CSE 413.
CSE419 Graph Theory
3 Credits
Introduction, Fundamental concepts, Trees, Spanning trees in graphs, Distance in graphs, Eulerian graphs, Digraphs, Matching and factors, Cuts and connectivity, kconnected graphs, Network flow problems, Graph coloring: vertex coloring and edge coloring, Line graphs, Hamiltonian cycles, Planar graphs, Perfect graphs.
Books Recommended:
 Graph Theory and Its Applications – Jonathan L. Gross, Jay Yellen
 A Textbook of Graph Theory – R. Balakrishnan, K. Ranganathan
CSE420 Graph Theory Lab
1.5 Credits
Laboratory works based on CSE 420.
CSE423 Computer System Performance Evaluations
3 Credits
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
CSE424 Computer System Performance Evaluation Lab
1.5 Credits
Laboratory based on CSE 423.
CSE437 Pattern Recognition
3 Credits
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
CSE438 Pattern Recognition Lab
1.0 Credits
Laboratory works based on CSE 437.
CSE453 Digital Image Processing
3 Credits
Image Processing: Image Fundamentals, Image Enhancement: Background, Enhancement by PointProcessing, Spatial Filtering, Enhancement in Frequency Domain, Color Image Processing. Image Restoration: Degradation Model, Diagonalization of Circulant and BlockCirculant Matrices, Algebraic Approach to Restoration, Inverse Filtering, Geometric Transformation. Image Segmentation: Detection of Discontinuities, Edge Linking and Boundary Detection, Thresholding, RegionOriented Segmentation, The use of Motion in Segmentation. Image Compression.
Books Recommended:
 Digital Image ProcessingRafael C. Gonzalez and Richard E. Woods, Pearson Education Asia.
 NonLinear Digital Filter : Principles and Applications –I. Pitas and A. N. Venetsanopoulos, Kluwer Academic Publications.
CSE454 Digital Image Processing Lab
1.5 Credits
Laboratory works based on CSE 453.
CSE455 Wireless and Sensor Networks
3 Credits
Introduction: applications; Localization and tracking: tracking multiple objects; Medium Access Control: SMAC, IEEE 802.15.4 and ZigBee; Geographic and energyaware routing; AttributeBased Routing: directed diffusion, rumor routing, geographic hash tables; Infrastructure establishment: topology control, clustering, time synchronization; Sensor tasking and control: taskdriven sensing, informationbased sensor tasking, joint routing and information aggregation; Sensor network databases: challenges, querying the physical environment, innetwork aggregation, data indices and range queries, distributed hierarchical aggregation; Sensor network platforms and tools: sensor node hardware, sensor network programming challenges; Other stateoftheart related topics.
Books Recommended:
 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
CSE456 Wireless and Sensor Networks Lab
1.5 Credits
Laboratory works based on CSE 455.
CSE457 BioInformatics
3 Credits
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, CValue 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, sitedirected 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. Structurebased 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 postorder 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.
Books Recommended:
 Introduction to Bioinformatics Algorithms –Jones and Pavel A. Pevzner
 Introduction to Bioinformatics – Stephen A. Krawetz, David D. Womble
 Introduction to Bioinformatics – Arthur M. Lesk
CSE458 BioInformatics Lab
1.5 Credits
Laboratory works based on CSE457.
CSE461 Neural Networks
3 Credits
Fundamentals of Neural Networks; Back propagation and related training algorithms; Hebbian learning; CohonenGrossberg 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; Selforganizing Maps; Spatiotemporal Pattern Classification, The Neocognition; Practical Aspects of Neural Networks.
Books Recommended:
 An Introduction to Neural Networks – Prof. Leslie Smith
 Fundamentals of Artificial Neural Networks – Mohamad H. Hassoun
CSE462 Neural Networks Lab
1.5 Credits
Laboratory works based on CSE 461.
CSE463 Machine Learning
3 Credits
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. Generaltospecific 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 crossvalidation, 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, VapnikChervonenkis dimension. Rule Learning, Propositional and FirstOrder: Translating decision trees into rules. Heuristic rule induction using separate and conquer and information gain. Firstorder Hornclause 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 nonlinear 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. InstanceBased Learning: Constructing explicit generalizations versus comparing to past specific examples. kNearestneighbor algorithm. Casebased 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. kmeans partitional clustering. Expectation maximization (EM) for soft clustering. Semisupervised learning with EM using labeled and unlabled data.
Books Recommended:
 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 Kernelbased Learning Methods, NelloCristianini and John ShaweTaylor, Cambridge University Press
CSE464 Machine Learning Lab
1.5 Credits.
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, Kmean 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.
CSE465 Contemporary course on CSE
03 Credits
CSE466 Contemporary course on CSE Lab
1.5 Credits
Laboratory works based on CSE 465