## Level III Course Units

**Note:** Students may take a maximum of 33 credites.Abbreviations : x – core courses, o – electives, L – lectures, P – practicals, C – credits

## Level IV Course Units

**Note:** Abbreviations : x – core courses, o – electives, L – lectures, P – practicals, C – credits

**ST 3003 : Marketing Research (30L, 2C)**

Syllabus:

Introduction, The Marketing Research Process, Defining the problem with exploratory research, Survey research: Methods of communication with respondents, Test marketing, Measurements and Attitude scaling, Questionnaire design, Sampling procedures, Data analysis, report writing and presentation: Stochastic models of brand choice, Applications of General Linear Models in marketing, Conjoint analysis, Correspondence analysis, Advertising media models, Marketing response models.

Introduction, The Marketing Research Process, Defining the problem with exploratory research, Survey research: Methods of communication with respondents, Test marketing, Measurements and Attitude scaling, Questionnaire design, Sampling procedures, Data analysis, report writing and presentation: Stochastic models of brand choice, Applications of General Linear Models in marketing, Conjoint analysis, Correspondence analysis, Advertising media models, Marketing response models.

Evaluation Criteria:

End-of-semester examination and assignments

Suggested Readings:

Marketing Research (D.R. Lehmann, S. Gupta, J.H. Steckel), Marketing Research, (Melvin Crask, Richard J. Fox, Roy Stout), Marketing Research (David A. Aakar)

Marketing Research (D.R. Lehmann, S. Gupta, J.H. Steckel), Marketing Research, (Melvin Crask, Richard J. Fox, Roy Stout), Marketing Research (David A. Aakar)

**ST 3007 – Operational Research (45L, 3C)**

Dependencies:

AM 2003, AM 2004

Syllabus:

Integer programming and solution techniques, Zero-one programming and solution techniques, Transportation models, Assignment models and their solution techniques. Project planning and evaluation techniques, Deterministic inventory models with shortages and without shortages, Queuing models, Different queuing systems and disciplines.

Integer programming and solution techniques, Zero-one programming and solution techniques, Transportation models, Assignment models and their solution techniques. Project planning and evaluation techniques, Deterministic inventory models with shortages and without shortages, Queuing models, Different queuing systems and disciplines.

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Operational Research an Introduction (Hamdy A. Taha), Operational research (Harvey M. Wagner)

Operational Research an Introduction (Hamdy A. Taha), Operational research (Harvey M. Wagner)

**ST 3051 – Statistical Inference I (45L, 3C)**

Dependencies:

AM 1001

Syllabus:

Characteristic function, Sampling from Normal population, sampling distributions of sample mean and sample variance (S2), independence of sample mean and S2, Estimation Criterion: Mean-squared error, Unbiasedness, Consistency, Sufficiency, Completeness, Efficiency, Factorization criterion. Variance Reduction: Cramer Rao Lower Bound, Rao-Blackwell Theorem, Lehmann- Scheffe’ Theorem. Methods of Estimation: Method of moments, Maximum Likelihood and Its Properties, Least Squares. Interval Estimation: Pivotal Method, General Method.

Characteristic function, Sampling from Normal population, sampling distributions of sample mean and sample variance (S2), independence of sample mean and S2, Estimation Criterion: Mean-squared error, Unbiasedness, Consistency, Sufficiency, Completeness, Efficiency, Factorization criterion. Variance Reduction: Cramer Rao Lower Bound, Rao-Blackwell Theorem, Lehmann- Scheffe’ Theorem. Methods of Estimation: Method of moments, Maximum Likelihood and Its Properties, Least Squares. Interval Estimation: Pivotal Method, General Method.

Evaluation Criteria:

Examinations and assignments

Suggested Readings:

Introduction to Mathematical Statistics (Hogg and Craig), Statistical Theory (Lindgren), Statistical Inference (Casella and Berger)

Introduction to Mathematical Statistics (Hogg and Craig), Statistical Theory (Lindgren), Statistical Inference (Casella and Berger)

**ST 3071 – Linear Models (45L, 3C)**

Syllabus:

Elementary linear and matrix algebra; Generalized and conditional inverses; Solutions of linear equations, idempotent matrices, trace of matrices; Derivatives of quadratic forms, expectation of random matrices; Multivariate normal distribution and its properties, distribution of quadratic forms; General linear model; optimal estimation and hypothesis testing, applications to regression model; Continued application of optimal inference; Design models, estimability, solving normal equations; Components of variance models and mixed models.

Elementary linear and matrix algebra; Generalized and conditional inverses; Solutions of linear equations, idempotent matrices, trace of matrices; Derivatives of quadratic forms, expectation of random matrices; Multivariate normal distribution and its properties, distribution of quadratic forms; General linear model; optimal estimation and hypothesis testing, applications to regression model; Continued application of optimal inference; Design models, estimability, solving normal equations; Components of variance models and mixed models.

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Theory and applications of the linear model (Graybill, F. A.), Matrices with applications in Statistics(Graybill, F. A.).

Theory and applications of the linear model (Graybill, F. A.), Matrices with applications in Statistics(Graybill, F. A.).

**ST 3072 – Applied Regression Analysis (45L, 3C)**

Dependencies:

ST1004, ST2004

Syllabus:

Introduction to regression, Correlation, Uses of regression, Simple linear regression model, Parameter estimation, inferences about the model, model diagnostics and prediction, Multiple regressions, Qualitative variables as predictors, Model building, Comparison of Regressions using dummy variables, Analysis of collinear data, Transformation of Variables, Polynomial Regression

Introduction to regression, Correlation, Uses of regression, Simple linear regression model, Parameter estimation, inferences about the model, model diagnostics and prediction, Multiple regressions, Qualitative variables as predictors, Model building, Comparison of Regressions using dummy variables, Analysis of collinear data, Transformation of Variables, Polynomial Regression

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Applied Regression Analysis (Draper & Smith), Applied Linear Regression (Sanford Weisberg), Introduction to Linear Regression Analysis (Montgomery, D.C. & Peck, E.A), Applied Regression Analysis (Draper, N.R. & Smith H.)

Applied Regression Analysis (Draper & Smith), Applied Linear Regression (Sanford Weisberg), Introduction to Linear Regression Analysis (Montgomery, D.C. & Peck, E.A), Applied Regression Analysis (Draper, N.R. & Smith H.)

**ST 3073 – Surveys and Sampling (45L, 3C)**

Syllabus:

Fundamentals of probability sampling and estimation, Simple Random Sampling: Theory involved in estimation procedures, Estimating population mean, variance, total & proportion, Estimating a ratio & its variance, Estimation using Ratio and Regression methods and their properties, Sample size determination. Stratified Random Sampling: Proportional and optimal cost allocations to strata, Estimating population mean, variance, total & proportion, Overview of advanced topics in stratified random sampling, Estimating a ratio & its variance, Regression estimators, Sample size determination. Post-stratification, Quota sampling, Cluster Sampling: Overview of cluster sampling, clustering with equal and unequal probabilities, Sample size determination, Design effect and intra-cluster correlation. Multi-stage sampling: Complex surveys and related problems, Sources of errors in surveys.

Fundamentals of probability sampling and estimation, Simple Random Sampling: Theory involved in estimation procedures, Estimating population mean, variance, total & proportion, Estimating a ratio & its variance, Estimation using Ratio and Regression methods and their properties, Sample size determination. Stratified Random Sampling: Proportional and optimal cost allocations to strata, Estimating population mean, variance, total & proportion, Overview of advanced topics in stratified random sampling, Estimating a ratio & its variance, Regression estimators, Sample size determination. Post-stratification, Quota sampling, Cluster Sampling: Overview of cluster sampling, clustering with equal and unequal probabilities, Sample size determination, Design effect and intra-cluster correlation. Multi-stage sampling: Complex surveys and related problems, Sources of errors in surveys.

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Elementary Sampling Theory (Vic Barnet), Survey Sampling (Leslie Kish), Sampling Techniques (William G. Cochran)

Elementary Sampling Theory (Vic Barnet), Survey Sampling (Leslie Kish), Sampling Techniques (William G. Cochran)

Syllabus:

Introduction: Definition, Types of time series, Components of time series, Time plot, Time series decomposition, Transformation, Differencing, Autocorrelation, Stationarity: Stationary & non-stationary time series, Tests for stationary, Modelling time series: Time series models, Model identification, Parameter estimation, Diagnostic checks, Forecasting, Spectral analysis

Introduction: Definition, Types of time series, Components of time series, Time plot, Time series decomposition, Transformation, Differencing, Autocorrelation, Stationarity: Stationary & non-stationary time series, Tests for stationary, Modelling time series: Time series models, Model identification, Parameter estimation, Diagnostic checks, Forecasting, Spectral analysis

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Forecasting Methods and Applications (Makridakis, S. Weelwright, S. C. and Hyndman, R. J.), The analysis of Time Series: An Introduction (Chatfield, C), Forecasting and Control (Box, G. E. P., Jenkins, G. M. and Reinsell)

Forecasting Methods and Applications (Makridakis, S. Weelwright, S. C. and Hyndman, R. J.), The analysis of Time Series: An Introduction (Chatfield, C), Forecasting and Control (Box, G. E. P., Jenkins, G. M. and Reinsell)

**ST 3075 – Design of Experiments (30L, 2C)**

Syllabus:

Principles of planning and designing comparative experiments; Review of ANOVA and related topics; Basic designs: completely randomized design (C.R.D), randomized complete block design (R. C. B. D), Latin squares/multiple Latin squares, treatment contrasts and mean comparisons; Factorial experiments (2k and others); confounding and partial confounding in 2k experiments; split-plot designs; analysis of covariance

Principles of planning and designing comparative experiments; Review of ANOVA and related topics; Basic designs: completely randomized design (C.R.D), randomized complete block design (R. C. B. D), Latin squares/multiple Latin squares, treatment contrasts and mean comparisons; Factorial experiments (2k and others); confounding and partial confounding in 2k experiments; split-plot designs; analysis of covariance

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Design and analysis of experiments (Montgomery, D. C.), Statistics for experiments: An introduction to design, data analysis and model building (Box, Hunter, and Hunter), The design of experiments (Mead, R).

Design and analysis of experiments (Montgomery, D. C.), Statistics for experiments: An introduction to design, data analysis and model building (Box, Hunter, and Hunter), The design of experiments (Mead, R).

**ST 3076 – Reliability Data Analysis (45L, 3C) **

Syllabus:

Reliability concepts and Reliability data, Models, censoring and likelihood for failure time data, Non-parametric estimation, Location-Scale based parametric distributions, Probability plotting, Parametric likelihood fitting concepts, Maximum likelihood estimates for the exponential mean based on the density approximation. Failure time regression analysis; failure time regression models, accelerated failure time models, regression diagnostics, proportional hazards model, weibull proportional hazards model. Accelerated test models: Different types of acceleration, accelerated life tests, methods of acceleration, acceleration models. Planning accelerated life tests: planning information, evaluation of test plans, planning single variable ALT experiments.

Reliability concepts and Reliability data, Models, censoring and likelihood for failure time data, Non-parametric estimation, Location-Scale based parametric distributions, Probability plotting, Parametric likelihood fitting concepts, Maximum likelihood estimates for the exponential mean based on the density approximation. Failure time regression analysis; failure time regression models, accelerated failure time models, regression diagnostics, proportional hazards model, weibull proportional hazards model. Accelerated test models: Different types of acceleration, accelerated life tests, methods of acceleration, acceleration models. Planning accelerated life tests: planning information, evaluation of test plans, planning single variable ALT experiments.

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Statistical methods for reliability data (Meeker, M.Q. and Escobar, L.A.), Reliability Modeling: A Statistical approach (Wolstenholme L.C.)

Statistical methods for reliability data (Meeker, M.Q. and Escobar, L.A.), Reliability Modeling: A Statistical approach (Wolstenholme L.C.)

**CS 3001 – Visual Programming Technologies (30L,30P, 3C)**

Conduct by University of Colombo School of Computing

**CS 3003 – Computer Graphics and Image Procesing (30L,30P, 3C)**

Conduct by University of Colombo School of Computing

Conduct by University of Colombo School of Computing

**CS 3008 – Introduction to Data Structures and Algorithems (30L,30P, 3C)**

Conduct by University of Colombo School of Computing

**ST 3053/ ST 3059 – Literature Review in ST / ST+CS (30P, 1C)**

Syllabus:

Read and discuss text/papers for a general sense of what research is/are about, how one thinks when doing research, and what the major research activities are, Identify research articles from different areas of statistics/computer science, involving different methodologies of research, and abstract them, Select an area related to statistics, which is of particular interest to you. Write a professional quality literature review for a problem of your choice.

Read and discuss text/papers for a general sense of what research is/are about, how one thinks when doing research, and what the major research activities are, Identify research articles from different areas of statistics/computer science, involving different methodologies of research, and abstract them, Select an area related to statistics, which is of particular interest to you. Write a professional quality literature review for a problem of your choice.

Evaluation Criteria:

Examinations

Dependencies:

ST1006

Syllabus:

Sampling plans of attribute type; OC curve scheme, Dodge and Romig approach, Decision theory approach, Double sampling plans, Sampling inspection by variables, Sequential sampling plans, Control charts; Variable type control charts, Attribute type control charts.

Sampling plans of attribute type; OC curve scheme, Dodge and Romig approach, Decision theory approach, Double sampling plans, Sampling inspection by variables, Sequential sampling plans, Control charts; Variable type control charts, Attribute type control charts.

Evaluation Criteria:

Examinations and assignments

Suggested Readings:

Statistical Quality Control (Mahajan, M), Statistical Quality Control, (Gupta, R.C.), Quality Control and Industrial Statistics (Duncan, A.J.)

Statistical Quality Control (Mahajan, M), Statistical Quality Control, (Gupta, R.C.), Quality Control and Industrial Statistics (Duncan, A.J.)

**ST 3070 – Special Topics (30L, 2C)**

Syllabus:

Selected topics depending on the availability of teaching staff.

Selected topics depending on the availability of teaching staff.

Evaluation Criteria: Examinations and assignments

**ST 3077 – Medical Statistics (45L, 3C)**

Syllabus:

Introduction, Epidemiology: Basic designs for epidemiological studies, relative risk and odds ratio, confounding and interaction. Analysis of data from cohort and case control studies, Matched case control studies, logistic regression. Clinical trials: Introduction, protocols for clinical trials, cross-over designs, allocation to treatment, sample size determination, Phase I and Phase II studies. Survival Analysis: Analysis of survival data, the survival and hazard functions. Non-parametric procedures: Kaplan-Meier estimate of survivor functions, log-rank test for comparing two survival times. Parametric modeling: Proportional hazards model, Cox’s proportional hazards model.

Introduction, Epidemiology: Basic designs for epidemiological studies, relative risk and odds ratio, confounding and interaction. Analysis of data from cohort and case control studies, Matched case control studies, logistic regression. Clinical trials: Introduction, protocols for clinical trials, cross-over designs, allocation to treatment, sample size determination, Phase I and Phase II studies. Survival Analysis: Analysis of survival data, the survival and hazard functions. Non-parametric procedures: Kaplan-Meier estimate of survivor functions, log-rank test for comparing two survival times. Parametric modeling: Proportional hazards model, Cox’s proportional hazards model.

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Statistical methods in medical research (Armitage, P.), Case-control studies ( Schlesselman, J.J.), Clinical trials (Pocock, S.J.), Modeling survival data in medical research (Collett, D.)

Statistical methods in medical research (Armitage, P.), Case-control studies ( Schlesselman, J.J.), Clinical trials (Pocock, S.J.), Modeling survival data in medical research (Collett, D.)

**ST 3078 – Case Studies/Assignments in ST (30P, 1C)**

Syllabus:

Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Evaluation Criteria:

Continuous Assignments

**ST 3079 – Case Studies/Assignments in ST + CS (30P, 1C)**

Syllabus:

Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Evaluation Criteria:

Continuous Assignments

**ST 3080 – Group Project in ST (30P, 1C)**

Syllabus:

Students will work as a team to achieve a common goal: finding a solution to a given problem. This will be done under the supervision of a suitable academic staff member.

Students will work as a team to achieve a common goal: finding a solution to a given problem. This will be done under the supervision of a suitable academic staff member.

Evaluation Criteria:

Report, Viva and Supervisor’s marks

** ST 3081 – Group Project in ST + CS (30P, 1C)**

Syllabus:

Students will work as a team to achieve a common goal: finding a solution to a given problem. This will be done under the supervision of a suitable academic staff member.

Evaluation Criteria:

Report, Viva and Supervisor’s marks

** PM 3001 – Real Analysis (45L, 3C)**

Conduct by Department of Mathematics

Contact Dean’s office

Syllabus:

Train students in analysing real/simulated data using latest statistical software: SPSS, SAS, MINTAB, STATISTICA, etc. The emphasis will be on applications of theory covered in the other subject areas as well as on presentation and report writing.

Train students in analysing real/simulated data using latest statistical software: SPSS, SAS, MINTAB, STATISTICA, etc. The emphasis will be on applications of theory covered in the other subject areas as well as on presentation and report writing.

Evaluation Criteria:

Examinations

**ST 4002 – Generalized Linear Models (30L, 2C)**

Dependencies:

ST 3051

Syllabus:

Generalized linear models: Continuous models, logit models, probit models, Model diagnostics, Biological assay, Over-dispersion, Quasi Likelihood models.

Generalized linear models: Continuous models, logit models, probit models, Model diagnostics, Biological assay, Over-dispersion, Quasi Likelihood models.

Evaluation Criteria:

End of semester written examination (70%), and practical and/or assignments (30%)

Suggested Readings:

Categorical Data Analysis (Alan Agresti), Modelling Binary Data (D. Collett) Generalised Linear Models (McCullah and Nelder)

Categorical Data Analysis (Alan Agresti), Modelling Binary Data (D. Collett) Generalised Linear Models (McCullah and Nelder)

**ST 4011 – Econometrics (30L, 2C)**

Syllabus:

Linear regression model and properties of least squares estimates; Autocorrelation; Heteroscadasity; Multicollinearity; Model specification; Simultaneous equations; Unit roots, Non stationary and Cointegration.

Linear regression model and properties of least squares estimates; Autocorrelation; Heteroscadasity; Multicollinearity; Model specification; Simultaneous equations; Unit roots, Non stationary and Cointegration.

Evaluation Criteria:

Examinations and assignments

Suggested Readings:

Econometric Model and Economic Forecasts (Robert Pindyck and Daniel Rubinfeld), Introductory Econometrics: A Modern Approach (Jeffrey Wooldridge), Econometric Analysis (Greene, W.G.), Econometric Methods (Jack Johnston and John DiNardo)

Econometric Model and Economic Forecasts (Robert Pindyck and Daniel Rubinfeld), Introductory Econometrics: A Modern Approach (Jeffrey Wooldridge), Econometric Analysis (Greene, W.G.), Econometric Methods (Jack Johnston and John DiNardo)

**ST 4016 – Catergorical Data Analysis (45L, 3C)**

Dependencies:

ST 4002

Syllabus:

Categorical response data, description and inference of two dimensional contingency tables, models for binary response variables, logistic regression, model diagnostics, Polytomous response variables, Log-linear models: Log-linear models for two dimensions, log-linear models for three or more dimensions, testing goodness of fit, estimation model parameters, iterative MLEs, hierarchical model fitting, diagnostics, partitioning chi-square to compare models, Strategies in model selection, Analysis of deviance, Testing conditional independence. Ordinal data: Log-linear models for ordinal variables, Models for ordinal variables in multidimensional tables.

Categorical response data, description and inference of two dimensional contingency tables, models for binary response variables, logistic regression, model diagnostics, Polytomous response variables, Log-linear models: Log-linear models for two dimensions, log-linear models for three or more dimensions, testing goodness of fit, estimation model parameters, iterative MLEs, hierarchical model fitting, diagnostics, partitioning chi-square to compare models, Strategies in model selection, Analysis of deviance, Testing conditional independence. Ordinal data: Log-linear models for ordinal variables, Models for ordinal variables in multidimensional tables.

Evaluation Criteria:

End of semester written examination (70%), and practical and/or assignments (30%)

Suggested Readings:

Categorical Data Analysis (Alan Agresti)

Analysis Ordinal Categorical Data (Alan Agresti)

Applied Logistic Regression (Hosmer and Lemeshow)

Categorical Data Analysis (Alan Agresti)

Analysis Ordinal Categorical Data (Alan Agresti)

Applied Logistic Regression (Hosmer and Lemeshow)

**ST 4030 – Multivariate Data Analysis (45L, 3C)**

Dependencies:

ST 4002

Syllabus:

Review of matrix algebra; Mean and variance- covariance of a random vector, correlation matrix; Properties of multivariate normal distribution and applications, checking for multivariate normality; Hypothesis testing using multivariate tests, tests on covariance matrices, tests of independence; Principal components analysis; Factor analysis; Discriminant analysis; Cluster analysis; Multivariate regression; MAOVA

Review of matrix algebra; Mean and variance- covariance of a random vector, correlation matrix; Properties of multivariate normal distribution and applications, checking for multivariate normality; Hypothesis testing using multivariate tests, tests on covariance matrices, tests of independence; Principal components analysis; Factor analysis; Discriminant analysis; Cluster analysis; Multivariate regression; MAOVA

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

Applied multivariate statistical analysis (Johnson and Wichern), Multivariate statistical methods (Morrison), Applied multivariate methods for data analysts (Johnson).

Applied multivariate statistical analysis (Johnson and Wichern), Multivariate statistical methods (Morrison), Applied multivariate methods for data analysts (Johnson).

**ST 4031 – Stochastic Processes and Applications (3C, 45L)**

Syllabus:

Generating functions, Convolution, Compounding, Random walks, Recurrent events, Discrete parameter Markov Chains, Continuous parameter Markov Chains, Birth and Death processes, Queuing processes.

Generating functions, Convolution, Compounding, Random walks, Recurrent events, Discrete parameter Markov Chains, Continuous parameter Markov Chains, Birth and Death processes, Queuing processes.

Evaluation Criteria:

End-of-semester examination and Assignments

Suggested Readings:

The Elements of Stochastic Processes(Bailey), An Introduction to Probability Theory and Applications (Feller), Stochastic Processes(Cox & Miller), Probability and Statistics with Reliability Queues and Computer Science Applications(Kishor S. Trivedi)

The Elements of Stochastic Processes(Bailey), An Introduction to Probability Theory and Applications (Feller), Stochastic Processes(Cox & Miller), Probability and Statistics with Reliability Queues and Computer Science Applications(Kishor S. Trivedi)

**ST 4040/ST 4050 – Individual Project in ST/ST+CS (180P(60P+120P), 6C)**

Syllabus:

The project topic could be selected from any area in the third and fourth year Statistics and/or Computer Science subject units. The selection of the project is done at the beginning of the year. The project will be done throughout the year and consists of six (6) progress reports (3 per each semester). Students are supposed to collect data for their individual projects from different Ministries/Research Institutes/Organizations, etc. They would be visiting these places during their fourth year for this purpose.

The project topic could be selected from any area in the third and fourth year Statistics and/or Computer Science subject units. The selection of the project is done at the beginning of the year. The project will be done throughout the year and consists of six (6) progress reports (3 per each semester). Students are supposed to collect data for their individual projects from different Ministries/Research Institutes/Organizations, etc. They would be visiting these places during their fourth year for this purpose.

Evaluation Criteria:

Examinations

Suggested Readings:

Statistical Theory (Lindgren)

Statistical Theory (Lindgren)

Conduct by University of Colombo School of Computing

**CS 4006 – Advanced Database Systems (45L,30P, 4C)**

Conduct by University of Colombo School of Computing

**CS 4011 – Natural Language Processing (45L,30P,4C)**

Conduct by University of Colombo School of Computing

**ST 4001 – Statistical Inference – II (30L, 2C)**

Dependencies:

ST 3051

Syllabus:

Parametric Inference: Introduction to Hypothesis Testing, Errors, Power, Neymann-Pearson Lemma, Most Powerful Tests, Uniformly Most Powerful Tests, Likelihood Ratio Tests: Sequential Tests; Sequential Probability Ratio Test (SPRT), Wald’s Identity, Average Sample Number (ASN). Distribution-free Inference: Tests of Randomness; Run Tests. One sample Location Tests for Median; Sign Test. Asymptotic Relative Efficiency (ARE), Two sample Location problem.

Parametric Inference: Introduction to Hypothesis Testing, Errors, Power, Neymann-Pearson Lemma, Most Powerful Tests, Uniformly Most Powerful Tests, Likelihood Ratio Tests: Sequential Tests; Sequential Probability Ratio Test (SPRT), Wald’s Identity, Average Sample Number (ASN). Distribution-free Inference: Tests of Randomness; Run Tests. One sample Location Tests for Median; Sign Test. Asymptotic Relative Efficiency (ARE), Two sample Location problem.

Evaluation Criteria:

Examinations and assignments

Suggested Readings:

Introduction to Theory of Statistics (Mood. Graybill and Boes), Statistical Theory (Lindgren)

Introduction to Theory of Statistics (Mood. Graybill and Boes), Statistical Theory (Lindgren)

**ST 4012 – Special Topics for ST (30L, 2C)**

Syllabus:

Selected topics depending on the availability of teaching staff.

Selected topics depending on the availability of teaching staff.

Evaluation Criteria:

Examinations and assignments

**ST 4013 – Special Topics For ST + CS (30L, 2C)**

Selected topics depending on the availability of teaching staff.

Evaluation Criteria:

Examinations and assignments

**ST 4015 – Decision Theory (30L, 2C)**

Dependencies:

ST 3051

Syllabus:

Convex Combinations, Utility, Personal probability. No Data Problem: Loss and Regret, Mixed Actions, Minimax Principle, Bayes Actions, Admissibility. Data in Decisions: Risk function, Estimation and Testing as Special cases, Properties of Decision Rules. Bayes Theorem: Posterior Distribution, Solving the Decision Problem, Conjugate Families, Estimation and Testing. Limiting distributions, laws of large numbers.

Convex Combinations, Utility, Personal probability. No Data Problem: Loss and Regret, Mixed Actions, Minimax Principle, Bayes Actions, Admissibility. Data in Decisions: Risk function, Estimation and Testing as Special cases, Properties of Decision Rules. Bayes Theorem: Posterior Distribution, Solving the Decision Problem, Conjugate Families, Estimation and Testing. Limiting distributions, laws of large numbers.

Evaluation Criteria:

Examinations and assignments

**ST 4032 – Case Studies in ST (30P, 1C)**

Syllabus:

Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Evaluation Criteria:

Continuous Assignments

Students will be given case studies/assignments which contain applications of theory covered in different courses followed by the students. The aims of this course it to enhance students’ analytical, presentation and writing skills.

Evaluation Criteria:

Continuous Assignments

**ST 4034 – Computational Statistics (3C, 45L)**

Syllabus:

Introduction to software package R. Introduction to Random numbers: pseudo random numbers, properties of random numbers, testing for basic properties, Software for random number generation; Introduction to Simulation: Simulation of random variables, Monte Carlo simulation methods, Simulation of inventory models, Simulation of Queuing models; Data Re-sampling: Introduction to Bootstrap, Bootstrap estimation of Variance, Bootstrap Confidence Intervals, Non-parametric bootstrap algorithm, Introduction to EM algorithm, Markov Chain Monte Carlo Methods

Introduction to software package R. Introduction to Random numbers: pseudo random numbers, properties of random numbers, testing for basic properties, Software for random number generation; Introduction to Simulation: Simulation of random variables, Monte Carlo simulation methods, Simulation of inventory models, Simulation of Queuing models; Data Re-sampling: Introduction to Bootstrap, Bootstrap estimation of Variance, Bootstrap Confidence Intervals, Non-parametric bootstrap algorithm, Introduction to EM algorithm, Markov Chain Monte Carlo Methods

Evaluation Criteria:

Examinations and assignments

Suggested Readings:

Computational Statistics (Givens, G. H. and Hoeting, J. A.), Elements of Computational Statistics (Gentle, J. E), An introduction to the bootstrap (Efron and Tibshirani).

Computational Statistics (Givens, G. H. and Hoeting, J. A.), Elements of Computational Statistics (Gentle, J. E), An introduction to the bootstrap (Efron and Tibshirani).

**CS 4003 – Logic Programming and Prolog (30L,30P, 3C)**

Conduct by University of Colombo School of Computing

**CS 4008 – Advanced Computer Graphics and Vision (30L,30P, 3C)**

Conduct by University of Colombo School of Computing

**CS 4017 – Wireless Ad-Hoc and Sensor Network (30L,30P, 3C)**

Conduct by University of Colombo School of Computing

**CS 4019 – Computational Pattern Recognition (45L,30P,4C)**

Conduct by University of Colombo School of Computing