Saturday, September 4, 2010
Statistics I
Course Title: Statistics I
Course no: STA-108 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: Concept of Applied Statistical Techniques and its Applications
Goal: This course makes students able to understand Applied Statistical Techniques and their applications in the allied areas.
Course Contents:
Unit 1: Sampling Techniques 10 Hrs.
Types of Sampling; Simple Random Sampling with and without Replacement; Stratified Random Sampling; Ratio and Regression Method of Estimation under Simple and Stratified Random Sampling; Systematic Sampling; Multistage Sampling; Estimation of population total and its Variance.
Unit 2: Non Parametric Test 16 Hrs.
Chi-square test: Test of goodness of fit; Test for independence (Categorical Data). Definition of Order Statistics; Run Test; Sign Test; Wilcoxon Matched Pairs Signed Ranks Test; Mann-Whitney U Test; Median Test; Kolmogorov Smirnov Test (One Sample Case); Cochran Q Test; Kruskl Wallis One way ANOVA Test; Friedman Two way ANOVA Test.
Unit 3: Correlation and Regression Analysis 19 Hrs.
Partial and Multiple Correlations; Multiple Linear Regressions: Assumptions; Coefficient Estimation, and Significance Test; Coefficient of Determination; Cobb-Dauglas Production Function; Growth Model; Logistic Regression; Autoregressive Model of order One, and Appraisal of Linear Models (Heteroscedasticity, Multicolinearity, Autocorrelation).
Note:
1. Theory and practice should go side by side.
2. It is recommended 45 hours for lectures and 15 additional hours for tutorial class for completion of the course in the semester.
3. SPSS Software should be used for data analysis.
4. Home works and assignments covering the lecture materials will be given throughout the semester.
Text Books:
· Draper, N. and H. Smith, Applied Regression Analysis, 2nd edition, New York: John Wiley & Sons, 1981.
· Hogg & Tanis, Probability & Statistical Inference, 6th edition, First Indian Reprint, 2002.
· Gujaratii, D. Basic Econometrics, International edition, 1995.
· Gibbons, J.D. Nonparametric Statistical Inference. International Student Edition.
· Siegel, S. Nonparametric Statistics for the Behavioural Sciences. McGraw-Hill, New York.
References:
· Hollander, M. & Wolfe, Nonparametric Statistical Methods. Johns Wiley & Sons, New York.
Applied Logic
Course Title: Applied Logic
Course no: CSC-306 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: This course contain the main feature of different logics.
Goal: The course objective is to provide the basic concepts and techniques of the logics used in computer science.
Course Contents:
Unit 1. Introduction 4 Hrs.
Introduction to Logic, Nature of Argument, Truth and Validity, Symbolic Logic, Statements, Conditional Statements, Statement Forms
Unit 2. Deduction and Deductive Systems 6 Hrs.
Formal Proof of Validity, The Rule of Replacement, The Rule of Conditional Proof, The Rule of Indirect Proof, Proofs of Tautologies, Formal Deductive Systems, Attribute of Formal Deductive Systems, Logicist Systems
Unit 3. Propositional Logic 6 Hrs.
Syntax of Propositional Logic, Semantics of Propositional Logic, Calculations, Normal Form, Applications
Unit 4. Predicate Logic 8 Hrs.
Predicate Logic, Order of Predicate Logic, Syntax of Predicate Logic, Semantics of Predicate Logic, Consequences, Calculations, Normal Form
Unit 5. Resolution & Proofs 10 Hrs.
Resolution, Resolution in Propositional Logic, Unification of Clauses, Resolution in Predicate Logic, Horn Clauses, Proof in Propositional Logic and Predicate Logic, Axiomatic Systems, Adequacy, Compactness, Soundness.
Unit 6. Program Verification 5 Hrs.
Issue of Correctness, Partial Correctness, Hoare Proof, Total Correctness.
Unit 7. Some Other Logics 6 Hrs.
Intuitionistic Logic, Lukasiewicz Logic, Probabilistic Logic, Fuzzy Logic, Default Logic, Autoepistemic Logic.
Laboratory works: Laboratory exercises should be conducted in any logic programming language like LISP or PROLOG.
Text / Reference books:
1. Arindama Singh, Logics for Computer Science, Prentice Hall of India
2. Irving M. Copi, Symbolic Logic, 5th Edition, Prentice Hall of India
Model Question of Simulation And Modeling.
Model Question:
Tribhuwan University
Bachelor of Computer Science and Information Techonolgy
Simulation and Modeling
Course Title : Simulation and Modeling
Course No : CSC-302
Credit Hours : 3
Nature of course : Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis : This course provides the discrete and continuous system,
generation of random variables, analysis of simulation output
Group A
Long Answer Questions. (Attempt any Two) 10 * 20 = 201.Define system modeling and simulation. Describe the dynamic physical model with suitable example
2.What do you mean by uniformity test? The following is the set of single digit numbers from a random number generator
| 6 | 7 | 0 | 6 | 9 | 9 | 0 | 6 | 4 | 6 |
| 4 | 0 | 8 | 2 | 6 | 6 | 1 | 2 | 6 | 8 |
| 5 | 6 | 0 | 4 | 7 | 1 | 3 | 6 | 0 | 7 |
| 1 | 4 | 9 | 8 | 6 | 0 | 9 | 6 | 6 | 7 |
| 1 | 0 | 4 | 7 | 9 | 2 | 0 | 1 | 4 | 8 |
| 6 | 9 | 7 | 7 | 5 | 4 | 2 | 3 | 3 | 3 |
| 6 | 0 | 5 | 8 | 2 | 5 | 8 | 8 | 3 | 1 |
| 4 | 0 | 8 | 1 | 7 | 0 | 0 | 6 | 2 | 8 |
| 5 | 6 | 0 | 8 | 0 | 6 | 9 | 7 | 0 | 0 |
| 3 | 1 | 5 | 4 | 3 | 8 | 3 | 3 | 2 | 4 |
Using appropriate test, check whether the numbers are uniformly distributed.
3.What do you understand by simulation output analysis? Describe the estimation method with suitable example.
Group B
Short Answer Questions (Attempt any Eight Questions)
1.Explain different phases of simulation study in brief.
2.Why do we use differential and partial differential equations in simulation?
3.define random number. Explain the rejection method of random number generation.
4. Explain the process of model validation through the iterative method of calibration.
5. Describe any 5 block diagram symbols of GPSS with suitable diagram.
6.What is Markov Chain? Describe different areas of application in short.
7. List out the entities, attributes, activities and state variables for the following systems:
a) Traffic system b) Banking system c) Super Markets.
d)Communication System e) Production System.
8.What do you mean by M/M/1/N/K ? Suppose an office working 8 hr per day for 5 days a week gets about 800 telephone calls a week. Find out the number of calls per minute.
9. Explain in brief real time simulation.
10. Write short notes on:
a) CSMP
b) Simulation Run Statistics
Committee:
1. Dr. Subarna Shakya -Expert and Cordinator -subarna40@gmail.com
2. Hari Khadka -Patan Campus - erkhadka@yahoo.com
3. Bhoj Raj Ghimire - Amrit Campus -bhojghimire614@gmail.com
4. Yadab Kattel - Katford -yadab4u@yahoo.com
5. Janak Raj Joshi - New Summit - joshi_janak_raj@yahoo.com
6. Karna Dev Bhatta - Siddhanath Science Campus - karndbhatta@gmail.com
Click for Related Syllabus:
Introduction to Cognitive Science
Course Title: Introduction to Cognitive Science
Course no: CSC-255 Full Marks: 60+20+20
Credit hours: 3 Pass Marks: 24+8+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: An introduction to cognitive science and its relation with other sciences. It covers briefly the area of Artificial Intelligence, Computational models and connectionist approach.
Goal:
· The student will gain an introductory understanding of what it means to say that intelligence is computational The student will:
o Acquire a good understanding of what an algorithm is and learn how to implement algorithms in the programming language LISP
o Develop an introductory understanding of formal models for computation, the limits of computation, the Chomsky hierarchy, and the Turing-Church hypothesis
· The student will study some of the modern attempts to demonstrate a computational model for intelligence through an introduction to the discipline of artificial intelligence, including introductions to knowledge representation, search, and artificial neural networks.
· Finally, the student will explore some of the positions taken in the ongoing discussion of this issue. In Philosophy and Linguistics, we will begin with Descartes, and look (and discuss) Turing, Gelernter, Newell and Simon, Penrose, Searle, and others, finishing with a partial response to Descartes given to us by Chomsky and others.
Course Contents:
Unit 1. Introduction to the Problem 6 Hrs.
Cognitive Science and other Science, Descartes, Marr, Algorithms and Computation, Turing's response to Descartes, Application related system in the Cognitive Science.
Unit 2. Brief Introduction to Artificial Intelligence 13 Hrs.
History and background of Artificial Intelligence, Knowledge representation, Human information processing and problem solving, Search, Expert system, Introduction of Neural Networks.
Unit 3. Computation 11 Hrs.
Introduction, Basic Model for Computation, The Turing Machine, Computational and Language: the Chomsky hierarchy, The Physical Symbols Systems Hypothesis, Illustration of practical examples.
Unit 4. Approaches 15 Hrs.
The connectionist approach, Different models and tool: Gelernter, Penrose, Pinker, Searle; Response to Descartes: Natural Language Processing, Parameters in the Natural Language Processing.
Text / Reference books:
1. Thinking about consciousness / David Papineau, Oxford: Clarendon Press New York: Oxford University Press, 2002.
2. Copeland, Jack: Artificial Intelligence: A Philosophical Introduction. Blackwell Publishers.
3. Cognition in a digital world / edited by Herre van Oostendorp, Mahwah, N.J.: L. Erlbaum Associates, 2003
4. The evolution and function of cognition / Felix Goodson, Mahwah, N.J.: Lawrence Erlbaum Associates, Publishers, 2003.
Introduction to Cognitive Science
Course Title: Introduction to Cognitive Science
Course no: CSC-255 Full Marks: 60+20+20
Credit hours: 3 Pass Marks: 24+8+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: An introduction to cognitive science and its relation with other sciences. It covers briefly the area of Artificial Intelligence, Computational models and connectionist approach.
Goal:
· The student will gain an introductory understanding of what it means to say that intelligence is computational The student will:
o Acquire a good understanding of what an algorithm is and learn how to implement algorithms in the programming language LISP
o Develop an introductory understanding of formal models for computation, the limits of computation, the Chomsky hierarchy, and the Turing-Church hypothesis
· The student will study some of the modern attempts to demonstrate a computational model for intelligence through an introduction to the discipline of artificial intelligence, including introductions to knowledge representation, search, and artificial neural networks.
· Finally, the student will explore some of the positions taken in the ongoing discussion of this issue. In Philosophy and Linguistics, we will begin with Descartes, and look (and discuss) Turing, Gelernter, Newell and Simon, Penrose, Searle, and others, finishing with a partial response to Descartes given to us by Chomsky and others.
Course Contents:
Unit 1. Introduction to the Problem 6 Hrs.
Cognitive Science and other Science, Descartes, Marr, Algorithms and Computation, Turing's response to Descartes, Application related system in the Cognitive Science.
Unit 2. Brief Introduction to Artificial Intelligence 13 Hrs.
History and background of Artificial Intelligence, Knowledge representation, Human information processing and problem solving, Search, Expert system, Introduction of Neural Networks.
Unit 3. Computation 11 Hrs.
Introduction, Basic Model for Computation, The Turing Machine, Computational and Language: the Chomsky hierarchy, The Physical Symbols Systems Hypothesis, Illustration of practical examples.
Unit 4. Approaches 15 Hrs.
The connectionist approach, Different models and tool: Gelernter, Penrose, Pinker, Searle; Response to Descartes: Natural Language Processing, Parameters in the Natural Language Processing.
Text / Reference books:
1. Thinking about consciousness / David Papineau, Oxford: Clarendon Press New York: Oxford University Press, 2002.
2. Copeland, Jack: Artificial Intelligence: A Philosophical Introduction. Blackwell Publishers.
3. Cognition in a digital world / edited by Herre van Oostendorp, Mahwah, N.J.: L. Erlbaum Associates, 2003
4. The evolution and function of cognition / Felix Goodson, Mahwah, N.J.: Lawrence Erlbaum Associates, Publishers, 2003.
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