# Mark Werness

Associate Professor
Degree
Ph.D., M.S., University of Minnesota
B.A., Carleton College
Office
OSS 424
Phone
(651) 962-5471
Toll Free
(800) 328-6819 ext. 5471
Mail
University of St. Thomas
Mail Number OSS 402
2115 Summit Avenue
St. Paul, MN 55105

## Professional Interests

My current academic interests are applied statistics, computer applications in experimental science, and STEM teacher preparation.  In addition, I was the first Program Director for the new Statistics major at UST, fall 2009 to summer 2011.

## Fall 2017 Courses

Fall 2017 Courses
Course - Section Title Days Time Location
STAT 220 - 06 Statistics I M - W - F - - 1055 - 1200 OSS 431
CRN: 40846 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201

## Schedule Details

Location Time Day(s)
STAT 220 - 07 Statistics I M - W - F - - 1215 - 1320 OSS 432
CRN: 40847 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201

## Schedule Details

Location Time Day(s)
STAT 400 - 01 Data Mining & Machine Learning - T - R - - - 1330 - 1510 OSS 431
CRN: 42577 4 Credit Hours Instructor: Mark E. Werness (Formerly IDTH 400) Introduction to statistical learning methods, from a statistical and computational perspective, to deal with massive and complex data. Topics include: Introduction; creating a project and diagram. Data preparation; defining and exploring data sources. Pattern discovery; cluster analysis, market basket analysis. Decision trees; cultivating and pruning decision trees, autonomous tree growth. Regression; transforming inputs, categorical inputs, polynomial regression. Neural Networks; input selection, stopped training. Model assessment; fit statistics, graphs, separate sampling. Model implementation; scored data sets, score code models. Applications. This course will give the basic ideas and intuition behind these methods, and special emphasis will be placed on their application through statistical software. Prerequisites: CISC 130 or 131, and MATH 113, and STAT 320 or 333.

## Schedule Details

Location Time Day(s)
STAT 460 - 03 Statistical Research/Practicum - - - - - - - -
CRN: 43340 4 Credit Hours Instructor: Mark E. Werness Students will work individually with the instructor to identify a statistical research topic of current interest or to identify a real practical problem, for which statistics can be used to produce a feasible solution. State and local governments, companies, businesses, TV channels, or even faculty doing research should be the natural source of real practical problems to be solved. For either the research or the practical problem, the final outcome should be a report with publication potential.

## Schedule Details

Location Time Day(s)

## J-Term 2018 Courses

J-Term 2018 Courses
Course - Section Title Days Time Location

## Spring 2018 Courses

Spring 2018 Courses
Course - Section Title Days Time Location
CISC 440 - 01 Artfcl Intelligence & Robotics M - W - - - - 1525 - 1700 OSS 415
CRN: 22275 4 Credit Hours Instructor: Mark E. Werness (Formerly QMCS 380) Theory and implementation techniques using computers to solve problems, play games, prove theorems, recognize patterns, create artwork and musical scores, translate languages, read handwriting, speak and perform mechanical assembly. Emphasis placed on implementation of these techniques in robots. Prerequisites: CISC 230 and STAT 220 (IDTH 220)

## Schedule Details

Location Time Day(s)
STAT 220 - 04 Statistics I M - W - F - - 1055 - 1200 OSS 432
CRN: 20706 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201

## Schedule Details

Location Time Day(s)
STAT 220 - 05 Statistics I M - W - F - - 1335 - 1440 OSS 333
CRN: 20707 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201

## Schedule Details

Location Time Day(s)