cs final year lesson plans

7/28/2019 CS Final Year Lesson Plans

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LESSON PLAN

Teacher Sarita Srivastav File Name : LP- EOE071-SS.doc

Subject Code EOE-071 Subject Name: Enterpreneurship Development

Session 2011-12 Groups : 08CSA, 08CSB,08EC,08IT

Lect. No. Ref. Pt. Topics to be covered

Unit I: Entrepreneurship

1 1.1 Entrepreneurship- Meaning & Defination, growth of SSI in developing

countries.2 1.2 SSI positions vis-a-vis large industries;

3 1.3 Role of small scale industries in the national economy

4 1.4 Characteristics and types of small scale industries

5 1.5 Demand based and resources based ancillaries and sub-control types.

6 1.6 Government policy for small scale industry; stages in starting a small scale

industry.

7 CASE STUDY

Unit II: Project identification

8 2.1 Assessment of viability, formulation, evaluation9 2.2 Financing, field-study and collection of information,

10 2.3 Preparation of project report, demand analysis,

11 2.4 Material balance and output methods

12 2.5 Benefit cost analysis, discounted cash flow,

13 2.6 Internal rate of return and net present value methods.

14 2.7 WORKSHEET

Unit III: Accountancy

15 3.1 Preparation of balance sheets16 3.2 Assessment of economic viability, decision making, expected costs

17 3.3 Planning and production control, quality control, marketing,

18 3.4 Industrial relations, sales and purchases, advertisement

19 3.5 Wages and incentive, inventory control

20 3.6 Preparation of financial reports, accounts and stores studies.

21 .

Unit IV: Project Planning and control

22 4.1 The financial functions, cost of capital approach in project planning and


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control

23 4.2.1 Economic evaluation, risk analysis,

24 4.2.2 capital expenditures, policies and practices in public enterprises

25 4.2.3 profit planning and programming, planning cash flow, capital expenditure

and operations

26 4.3 control of financial flows, control and communication.

Unit V: Laws concerning entrepreneur

27 5.1 Laws concerning entrepreneur viz, partnership laws

28 5.2 , business ownership, sales and income taxes

29 5.3 workman compensation act

30 5.4 Role of various national and state agencies which render assistance tosmall scale industries

31 Doubt Session

LESSON PLANTeacher Krishan Kumar File Name: LP-ECS074-KK.doc

Subject Code ECS-074 Subject Name Pattern Recognition

Session 2011-12 Group 08CSA and 08CSB

Lect.No.

Ref. Pt. Topics to be covered

Unit I: INTRODUCTION

1 1.1

1.2

1.3

Introduction of Pattern Recognition

Basics of pattern recognition

Examples

2 1.4

1.5

1.6

1.7

1.81.9

1.10

1.11

Design principles of pattern recognition system,Model

Preprocessing

Segmentation

Feature ExtractionTraining Samples

Cost

Generalization

3 1.12

1.13

1.14

1.15

1.16

1.17

Leaning and adaptation,

Supervised, unsupervised and reinforcement learning

Pattern recognition approaches:Template matching

Statistical classification

Syntactic or structural matching


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1.18 Neural networks

4

1.13

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Mathematical foundation:-

Linear algebra:Probability theory:

5

1.15

1.16


expectation,

mean and covariance,

6

1.17

1.18


Normal distribution,

multivariate normal densities,

7

1.19

1.20

Mathematical foundation:-Chi squared test

Assignment-18 1.21 Class Test-1 Marks: 10

Unit- II: Statistical Pattern Recognition

9

2.1

2.2

2.3

Bayesian Decision Theory:

Introduction

Bayesian Decision Theory -Continuous FeaturesMinimum-Error-Rate classification

10 2.4

2.5

2.6

Classifiers, discriminants

Multi Category case

Two Category case

11 2.7

2.8

2.9

Normal Density

Univariate Density

Multivariate Density

12 2.10

2.11

2.12

2.13

Normal density and discriminants function

Case-1

Case-2

Case-3

13 2.14

2.15

2.16

Error Bounds for Normal DensitiesChernoff Bound

Bhattacharyya Bound

14 2.17

2.18Bayes Decision Theory Discrete FeaturesIndependent Binary Features

15 2.19

2.20

2.21

Missing and Noisy FeaturesMissing Features

Noisy Features

16 2.22

2.23Compound Bayes Decision Theory and Context

Assingment-2

17 2.24 Class Test-2 Marks: 10


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Unit- III: Parameter Estimation Methods

18

3.1

3.2

3.3

3.4

Parameter Estimation Methods

IntroductionMaximum likelihood estimation,

General PrincipalThe Gaussian case

19 3.5

3.6

3.7

3.8

3.9

3.10

3.11

Bayesian Estimation-

Class Conditional DensitiesParameter Distribution

Bayesian parameter estimation: General TheoryBayesian parameter estimation: Gaussian Case

The Univariate Case:p(|D)

The Univariate Case:p(x|D)

The Multivariate Case20 3.12

3.13

3.14

Dimension Reduction methods-Problems of Dimensionality

Principal component Analysis (PCA),

21 3.15 Fisher Linear discriminants analysis,

22 3.16 Expectation-Maximization (EM),

23 3.17

3.18

3.193.20

Hidden Markov models (HMM),First-order Markov models

First-order hidden Markov models

Hidden Markov Model ComputationEvaluation

Examples

24 3.21 Gaussian mixture models

25 3.22 Revision of whole unitAssignment-3


UNIT-4 :Non parametric techniques:

27 4.1

4.2

Introduction

Density estimation,

28 4.3

4.4

4.5

4.6

Parzen windows,IntroductionConvergence of the MeanConvergence of the MeanClassification example

29 4.7

4.8

K-Nearest Neighbor Estimation,Estimation of a posteriori probabilities

30 4.9

4.10

4.11

Nearest Neighbor Rule,Convergence of the Nearest NeighborError Rate for the Nearest-Neighbor RuleError Bounds


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4.12

4.13

The k-Nearest-Neighbor RuleComputational Complexity of the kNearest-Neighbor Rule

31 4.14

4.15

4.16

Fuzzy classificationAre Fuzzy Category Memberships just Probabilities?Assignment-4


UNIT-5 Unsupervised learning and clustering:

33 5.1 Creation function for clustering,

34 5.2

5.3

Clustering Techniques:

Iterative square-error partitional clustering-K means,

35 5.4 Agglomerative hierarchical clustering,36 5.5 Clustering validation

37 5.6 Assignment-5, class Test-5 Marks: 10

38 5.7 Revision using PPTs

39 5.8 PPT Showing some development in this field using Research Papers

40 5.9 Queries Handling

BOOKS

1 Pattern Classification by Richard O. Duda, Peter E. Hart, Devid G. Stork,

II Edition

2 Pattern Recognition and Machine Learning by Cristopher M. Bishop

3 Foundation of Statistical Natural Language Processing by Cristopher D.

Manning and Hinrich Schutze

LESSON PLAN

Teacher Sapna Katiyar File Name LP-ECS702-SK

Subject Code ECS702 Subject Name Digital Image Processing

Session 2011 Group 08CSA, 08CSB

Lect.

No.

Ref.

Pt.

Topics to be covered

Unit I: Introduction & Fundamentals

1 1.1

1.2

Motivation and Perspective & Applications

Components of Image Processing System

2 1.3

1.4

Element of Visual Perception


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A Simple Image Model

3 1.5 Sampling and Quantization

4 1.6

1.7

Basic Gray Level Functions Piecewise-Linear Transformation Functions

Contrast Stretching; Histogram Specification

5 1.8 Histogram Equalization; Local Enhancement

6 1.9 Enhancement using Arithmetic/Logic Operations Image Subtraction, Image

Averaging

7 1.10 Basics of Spatial Filtering; Smoothing - Mean filter, Ordered Statistic Filter

8 1.11 Sharpening The Laplacian

Unit II: Image Enhancement in Frequency Domain

9 2.1

2.2

Fourier Transform and the Frequency Domain

Basis of Filtering in Frequency Domain

10 2.3 Filters Low-pass, High-pass

11 2.4

2.5

Correspondence Between Filtering in Spatial and Frequency Domain

Smoothing Frequency Domain Filters Gaussian Low pass Filters

12 2.6 Sharpening Frequency Domain Filters Gaussian Highpass Filters

13 2.7 Homo morphic FilteringImage Restoration

14 2.8

2.9

A Model of Restoration Process

Noise Models

15 2.10

2.11

Restoration in the presence of Noise only-Spatial Filtering Mean Filters

Arithmetic Mean filter

16 2.12

2.13

2.14

Geometric Mean Filter

Order Statistic Filters Median Filter

Max and Min filters

17 2.15

2.16

Periodic Noise Reduction by Frequency Domain Filtering Band pass Filters

Minimum Mean-square Error RestorationUnit III: Color Image Processing

18 3.1

3.2

3.3

Color Fundamentals,

Color Models

Converting Colors to different models

19 3.4

3.5

Color Transformation

Smoothing and Sharpening

20 3.6 Color Segmentation

Morphological Image Processing

21 3.7 Introduction


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3.8 Logic Operations involving Binary Images

22 3.9

3.10

Dilation and Erosion

Opening and Closing

23 3.11 Morphological Algorithms Boundary Extraction

24 3.12

3.13

Region Filling

Extraction of Connected Components

25 3.14

3.15

Convex Hull

Thinning, Thickening

Unit IV: Registration

26 4.1

4.2

Introduction

Geometric Transformation Plane to Plane transformation

27 4.3 Mapping, Stereo Imaging Algorithms to Establish Correspondence

28 4.4 Algorithms to Recover Depth

Segmentation

29 4.5 Introduction, Region Extraction

30 4.6

4.7

Pixel-Based Approach

Multi-level Thresholding

31 4.8

4.9

Local Thresholding

Region-based Approach

32 4.10

4.11

Edge and Line Detection: Edge Detection, Edge Operators

Pattern Fitting Approach

33 4.12

4.13

Edge Linking and Edge Following

Edge Elements Extraction by Thresholding

34 4.14 Edge Detector Performance

Unit V: Feature Extraction

35 5.1

5.2

5.3

Representation

Topological Attributes

Geometric Attributes

Description

36 5.4 Boundary-based Description

37 5.5 Region-based Description, Relationship

Object Recognition

38 5.6 Deterministic Methods, Clustering

39 5.7

5.8

Statistical Classification

Syntactic Recognition

40 5.9

5.10

Tree Search

Graph Matching


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LESSON PLANTeacher: Shruti Mittal File Name: LP-TCS801-SM.doc

Subject Code: TCS-801 Subject Name : Distributed Systems

Session: 2011-12 Group: 08CSA&08CSB

Lect.

No.

Ref. Pt. Topics to be covered

Unit I:

1 1.1 Characterization of Distributed Systems: Introduction, Examples ofdistributed Systems,Theoretical Foundation for Distributed System: Limitation of

Distributed system, absence of global clock, shared memory2 1.2 Resource sharing and the Web Challenges.

System Models: Architectural models, Fundamental Models

3 1.3 Logical clocks, Lamports logical clocks

4 1.4 Vectors logical clocks

5 1.5 Causal ordering of messages, global state, termination detection.

6 1.6 Distributed Mutual Exclusion: Classification of distributed mutual

exclusion, requirement of mutual exclusion theorem,

7 1.7 Token based and non token based algorithms, Performance metric fordistributed mutual exclusion algorithms.

Unit II:

8 2.1 Distributed Deadlock Detection: system model, resource Vscommunication deadlocks, deadlock prevention

9 2.2 Deadlock avoidance, detection & resolution, centralized dead lockdetection,

10 2.3 distributed dead lock detection, path pushing algorithms, edge chasing

algorithms.

11 2.4 Agreement Protocols: Introduction, System models, classification ofAgreement Problem

12 2.5 Byzantine agreement problem, Consensus problem, InteractiveconsistencyProblem

13 2.6 Solution to Byzantine Agreement problem, Application of Agreementproblem, Atomic Commit in Distributed Database system.

Unit III:

14 3.1 Distributed Objects and Remote Invocation: Communication between

distributed objects, Remote procedure call, Events and notifications, JavaRMI case study.

15,16 3.2 Security: Overview of security techniques, Cryptographic algorithms,Digital signatures


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17,18 3.2 Cryptography pragmatics, Case studies: Needham Schroeder, Kerberos,SSL & Millicent.

19,20 3.3 Distributed File Systems: File service architecture, Sun Network FileSystem, The Andrew File System, Recent advances.

Unit IV:

21 4.1 Transactions and Concurrency Control: Transactions, Nested

transactions, Locks, Optimistic Concurrency control

22,23 4.2,4.3 Timestamp ordering, Comparison of methods forconcurrency control.

24,25,

26

4.4 Distributed Transactions: Flat and nested distributed transactions,Atomic Commit protocols, Concurrency control in distributed

transactions, Distributed deadlocks,

27,28 4.5 Transaction recovery. Replication: System model and groupcommunication.

29,30 4.6 Fault - tolerant services, highly available services, Transactions with

replicated data.

Unit V:

31,32 5.1 Distributed Algorithms: Introduction to communication protocols,Balanced sliding window protocol,

33,34 5.2 Routing algorithms, Destination based routing, APP problem,

35,36 5.3 Deadlock free Packet switching, Introduction to Wave & traversal

algorithms

37,38 5.4 Election algorithm.CORBA Case Study: CORBA RMI, CORBAservices.

LESSON PLAN

Teacher Rajdev Tiwari

Subject Code ECS-075Subject Name Data Mining and WarehousingSession 2011-12Group 08CS & 08IT

Lect. No(s) Ref. No(s) Topics to be covered with reference points

Unit I: Overview1 1.1 Motivation (for Data Mining),Data Mining-

Definition & Functionalities2 1.2 Data Processing, Form of Data Preprocessing, Data

Cleaning: Missing Values


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3 1.3 Data Cleaning: Missing Values, Noisy

Data,(Binning, Clustering, Regression, Computer

and Human inspection)4 1.4 Inconsistent Data, Data Integration and

Transformation.5 1.5 Data Reduction:-Data Cube Aggregation,

Dimensionality reduction, Data Compression6 1.6 Numerosity Reduction, Clustering, Discretization

and Concept hierarchy generation

Unit II: Concept Description7 2.1 Definition, Data Generalization, Analytical

Characterization, Analysis of attribute relevance8 2.2 Mining Class comparisons, Statistical measures in

large Databases. Measuring Central Tendency9 2.3 Measuring Dispersion of Data, Graph Displays of

Basic Statistical class Description10 2.4 Mining Association Rules in Large Databases,

Association rule mining11 2.5 Mining Single-Dimensional Boolean Association

rules from Transactional Databases Apriori

Algorithm12 2.6 Mining Multilevel Association rules from

Transaction Databases13 2.7

Mining Multi-Dimensional Association rules fromRelational Databases

Unit III: Classification and prediction14 3.1 What is Classification & Prediction, Issues regarding

Classification and prediction15 3.2 Decision tree16 3.3 Bayesian Classification17 3.4 Classification by Back propagation, Multilayer feed-

forward Neural Network, Back propagation

Algorithm

18 3.5 Classification methods K-nearest neighborclassifiers, Genetic Algorithm

19 3.6 Cluster Analysis: Data types in cluster analysis,

Categories of clustering methods, Partitioning

methods20 3.7 Hierarchical Clustering- CURE and Chameleon21 3.8 Density Based Methods-DBSCAN, OPTICS,22 3.9 Grid Based Methods- STING, CLIQUE

23 3.10 Model Based Method Statistical Approach, Neural

Network approach


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24 3.11 Outlier Analysis

Unit IV: Data Warehousing

25 4.1 Overview, Definition, Delivery Process,

26 4.2 Difference between Database System and Data

Warehouse, Multi Dimensional Data Model27 4.3 Data Cubes, Stars, Snow Flakes, Fact Constellations28 4.4 Concept hierarchy, OLAP operations*29 4.5 Process Architecture, 3 Tier Architecture30 4.6 Data Marting31 4.7 Granularity and level of summarization*

Unit V:

32 5.1 Aggregation, Historical information, Query Facility33 5.2 OLAP function and Tools. OLAP Servers, ROLAP,

MOLAP, HOLAP34 5.3 Data Mining interface, Security35 5.3 Backup and Recovery, Tuning Data Warehouse36 5.4 Testing Data Warehouse

References

1. M.H.Dunham,Data Mining:Introductory and

Advanced Topics PearsonEducation

2. Jiawei Han, Micheline Kamber, Data Mining

Concepts & Techniques Elsevier3. Sam Anahory, Dennis Murray, Data

Warehousing in the Real World : A Practical Guide

for Building Decision Support Systems, Pearson

Education

4. Mallach,Data Warehousing System,McGraw

Hill


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LAB PLAN

Teacher MANISH MISHRA

Subject Code ECS-752

Subject Name DIGITAL IMAGE PROCESSING LAB

Session 2011-12

Group 08CSA

A. LIST OF EXPERIMENTS AS PER UNIVERSITY SYLLABUS

Class

No.

Name of experiment Tentative Date

1. Write a program in c to rotate a line in animated

form

5Aug

2. Write a c program to fill a rectangle using eight

neighbor pixel algo

12Aug

3. Write a c program to display intensity array ofoutput image for given input image using salt andpeper noise method

19Aug

4. Write a c program to display intensity array of

output image for given input image using histogram

equalization method

26Aug

5. Write a c program to implement image enhancementusing arithmetic and logical operator (i.e.subtraction

averaging of image)

2Sep

6. Write a c program to implement imageSegmentation

9Sep

7. Write a c program to implement Region extractionof an input image

16Sep

8. Write a c program to implement morphologicalimage processing 23Sep

9. Write a c program to implement Colortransformation on input image

14Oct

10. Write a c program to implement2Dtransformation(translation and rotation) on input

image

21Oct

11. Implement all the above methods by using

MATLAB

4Nov,11Nov,18Nov


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B. LAB CONDUCT PLAN:All the groups shall conduct same experiment on a day (when implement with c).

ORThe students shall be divided in groups of 2/3(when implement with MATLAB) and

each group shall conduct different experiments.

C. LAB EVALUATION/MARKING SCHEMETotal internal marks: 25

Attendance marks: 5

Lab Record: 5

Quiz 5

Internal Viva 10

Teacher MANISH MISHRA

Subject Code ECS-752

Subject Name DIGITAL IMAGE PROCESSING LAB

Session 2011-12

Group 08CSB

D. LIST OF EXPERIMENTS AS PER UNIVERSITY SYLLABUS

Class

No.

Name of experiment Tentative

Date

1. Write a program in c to rotate a line in animated

form

1Aug

2. Write a c program to fill a rectangle using eight

neighbor pixel algo

8Aug

3. Write a c program to display intensity array ofoutput image for given input image using salt andpeper noise method

29Aug

4. Write a c program to display intensity array of

output image for given input image using histogram

equalization method

12Sep

5. Write a c program to implement image enhancement

using arithmetic and logical operator (i.e.subtraction

averaging of image)

19Sep

6. Write a c program to implement image

Segmentation

26Sep


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7. Write a c program to implement Region extractionof an input image

10Oct

8. Write a c program to implement morphologicalimage processing 10Oct

9. Write a c program to implement Colortransformation on input image

17Oct

10. Write a c program to implement

2Dtransformation(translation and rotation) on input

image

31Oct

11. Implement all the above methods by using

MATLAB

31Oct,14Nov

E. LAB CONDUCT PLAN:All the groups shall conduct same experiment on a day (when implement with c).

OR

The students shall be divided in groups of 2/3(when implement with MATLAB) and

each group shall conduct different experiments.

F. LAB EVALUATION/MARKING SCHEMETotal internal marks: 25

Attendance marks: 5Lab Record: 5

Quiz 5

Internal Viva 10

cs final year lesson plans

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