Teaching

"Time is the best teacher. Unfortunately, it kills all of its students."

- Hector Berlioz [1803-1869]

SPRING 2026

FALL 2025

DATA440 - Machine Learning
This course provides a broad introduction to automated learning from data. Machine learning is the name given to the collection of techniques that allow computational systems to adaptively improve their performance by learning from past observed data. The course introduces the theoretical underpinnings of learning from data, the study of learning algorithms, as well as machine learning applications. Topics include: supervised and unsupervised learning (including linear models, support vector machines, decision trees, PCA, neural networks) , regularization methods, validation and model selection. An introduction to deep learning will be included. The Python language and several Python libraries will be used to illustrate various machine learning techniques.
Sections:
DATA440L111/MSCS505N232 : Mon 3:30PM-6:15PM, Wed 9:30PM-10:45PM

MSIS645 - Data Mining & Predictive Analytics
Data Mining & Predictive Analytics is the name given to a group of disciplines, technologies, applications and practices for analyzing data (usually based on past business performance) and building models to help enterprise users make better, faster business decisions. This course introduces basic concepts, tasks, methods, and techniques in data mining, including data exploration and pre-processing, classification, regression, clustering, association, performance evaluation applied to predictive modeling. The Python language and several Python libraries will be used throughout the course.
Sections:
MSIS645L711: online
MSIS537L232: online / Mon 11:00AM-12:15PM

MSIS537 - Data Management I
Database Management Systems (DBMSs) support the development and use of databases by facilitating data insertion, update, retrieval and integrity. Successful IS professionals must know how to design a sound database. In addition, IS professionals must know how to retrieve and update data through a DBMS. This course examines the theories and concepts employed in database management systems (DBMS) and the efficiencies and economics of such systems. The function of various types of DBMS are described including their purpose, advantages, disadvantages, and applications in business. The course explores the following topics: DBMS architectures, data modeling, database normalization, relational algebra, SQL, client/server systems, DB physical design, multiple user environments, database security, data warehousing architectures, dimensional modeling, online analytical processing (OLAP).
Sections:
MSIS537L232: Wed 2:00PM-4:45PM

SPRING 2025

On sabbatical leave.

FALL 2024

MSIS645 - Data Mining & Predictive Analytics
Data Mining & Predictive Analytics is the name given to a group of disciplines, technologies, applications and practices for analyzing data (usually based on past business performance) and building models to help enterprise users make better, faster business decisions. This course introduces basic concepts, tasks, methods, and techniques in data mining, including data exploration and pre-processing, classification, regression, clustering, association, performance evaluation applied to predictive modeling. The Python language and several Python libraries will be used throughout the course.
Sections:
MSIS645L711: online
MSIS645L256: Thu 11AM–12:15PM, and online

MSIS537 - Data Management I
Database Management Systems (DBMSs) support the development and use of databases by facilitating data insertion, update, retrieval and integrity. Successful IS professionals must know how to design a sound database. In addition, IS professionals must know how to retrieve and update data through a DBMS. This course examines the theories and concepts employed in database management systems (DBMS) and the efficiencies and economics of such systems. The function of various types of DBMS are described including their purpose, advantages, disadvantages, and applications in business. The course explores the following topics: DBMS architectures, data modeling, database normalization, relational algebra, SQL, client/server systems, DB physical design, multiple user environments, database security, data warehousing architectures, dimensional modeling, online analytical processing (OLAP).
Sections:
MSIS537L256: Wed 6:30PM-9:00PM

SPRING 2024

FALL 2023

MSIS645 - Data Mining & Predictive Analytics
Data Mining & Predictive Analytics is the name given to a group of disciplines, technologies, applications and practices for analyzing data (usually based on past business performance) and building models to help enterprise users make better, faster business decisions. This course introduces basic concepts, tasks, methods, and techniques in data mining, including data exploration and pre-processing, classification, regression, clustering, association, performance evaluation applied to predictive modeling. The Python language and several Python libraries will be used throughout the course.
Sections:
MSIS645L256: Mon 11AM–12:15PM, Wed 9:30AM–10:45AM

DATA440 - Machine Learning
This course provides a broad introduction to automated learning from data. Machine learning is the name given to the collection of techniques that allow computational systems to adaptively improve their performance by learning from past observed data. The course introduces the theoretical underpinnings of learning from data, the study of learning algorithms, as well as machine learning applications. Topics include: supervised and unsupervised learning (including linear models, support vector machines, decision trees, PCA, neural networks) , regularization methods, validation and model selection. An introduction to deep learning will be included. The Python language and several Python libraries will be used to illustrate various machine learning techniques.
Sections:
DATA440L111 : Mon 3:30PM-6:15PM, Wed 3:30PM-4:45PM

SPRING 2023

MSIS645/DATA450 -Data Mining & Predictive Analytics
Data Mining & Predictive Analytics is the name given to a group of disciplines, technologies, applications and practices for analyzing data (usually based on past business performance) and building models to help enterprise users make better, faster business decisions. This course introduces basic concepts, tasks, methods, and techniques in data mining, including data exploration and pre-processing, classification, regression, clustering, association, performance evaluation applied to predictive modeling. The Python language and several Python libraries will be used throughout the course.
Sections:
MSIS645L711: online
DATA450L111: Wed 2PM-4:45PM
DATA450L700: online

CMPT428 -Data & Information Management
This course aims to introduce the technologies and disciplines responsible for the effective management of structured and unstructured data and information in organizations. Topics covered include data sourcing, extraction transformation and loading processes, data warehousing architectures, dimensional modeling, online analytical processing, businessperformance management, dashboards and visualization, and massive (Big) data processing. We will start by tackling structured data in a relational and multidimensional environment. In the second part of the semester we will tackle Big Data processing: distributed computing, the Hadoop ecosystem and Apache Spark.
Sections:
CMPT428L111: Mon 3:30PM-6:15PM

FALL 2022
MSIS645/DATA450 - Data Mining & Predictive Analytics
DATA440 - Machine Learning

SPRING 2022
MSIS645/DATA450 -Data Mining & Predictive Analytics

FALL 2021 MSIS645/DATA450 - Data Mining & Predictive Analytics
DATA440 - Machine Learning

SPRING 2021
MSIS645/DATA450 -Data Mining & Predictive Analytics

FALL 2020 MSIS645/DATA450 - Data Mining & Predictive Analytics
DATA440 - Machine Learning

SPRING 2020
MSIS645/DATA450 -Data Mining & Predictive Analytics

FALL 2019
CMPT489/MSCS589 - Special Topic: Natural Language Processing & Text Mining
MSIS645/DATA450 - Data Mining & Predictive Analytics

SPRING 2019
MSIS645/DATA450 - Data Mining & Predictive Analytics

FALL 2018
MSIS645/DATA450 - Data Mining & Predictive Analytics
CMPT428 - Data & Information Management

SPRING 2018
MSIS645/DATA450 - Data Mining & Predictive Analytics

FALL 2017
On sabbatical leave

SPRING 2017
MSIS645/DATA450 - Data Mining & Predictive Analytics
MSIS637 / ITS452 - Decision Support Systems

FALL 2016
CMPT428/MSIS591 - Data & Information Management
MSIS537 - Data Management I

SPRING 2016
MSIS645/ITS481 -Data Mining & Predictive Analytics
MSIS637 / ITS452 - Decision Support Systems

FALL 2015
CMPT428/MSIS591 - Data & Information Management
MSIS537 - Data Management I

SPRING 2015
MSIS645/ITS481 - Data Mining & Predictive Analytics
MSIS637 / ITS452 - Decision Support Systems

FALL 2014
MSIS638/ITS438 - IS Business Intelligence
ITS452 / MSIS637 - Decision Support Systems

SPRING 2014
MSIS645/ITS481 - Data Mining & Predictive Analytics
MSIS637 / ITS452 - Decision Support Systems

FALL 2013
MSIS638/ITS438 - IS Business Intelligence
ITS452 / MSIS637 - Decision Support Systems

SPRING 2013
ITS408 / MSIS537 - Data Management I
MSIS591/ITS378 - Data Mining & Predictive Analytics

FALL 2012
MSIS638/ITS438 - IS Business Intelligence
ITS452 / MSIS637 - Decision Support Systems

SPRING 2012
ITS408 / MSIS537 - Data Management I
MSIS591/ITS378 -Data Mining & Predictive Analytics

FALL 2011
MSIS638/ITS438 - IS Business Intelligence
MSCS550 / CMS404 -Artificial Intelligence

SPRING 2010
On sabbatical leave

FALL 2010
MSIS591/ITS378 - Data Mining & Predictive Analytics
MSIS638/ITS438 - IS Business Intelligence
MSCS550 / CMS404 -Artificial Intelligence
ITS452 / MSIS637 - Decision Support Systems

SPRING 2010
ITS408 / MSIS537 - Data Management I
MSIS638/ITS438 -IS Business Intelligence

FALL 2009
ITS452 / MSIS637 - Decision Support Systems
MSIS527 - Systems and Information Concepts in Organizations

Spring 2009
ITS408 / MSIS537 - Data Management I
MSIS638/ITS438 -IS Business Intelligence

Fall 2008
MSIS527 - Information & Systems Concepts in Organizations
ITS452/ MSIS637 - Decision Support Systems

Spring 2008
MSIS527 - Information & Systems Concepts in Organizations
MSIS638/ITS438 -IS Business Intelligence

Fall 2007
ITS408 / MSIS537 - Data Management I
ITS452/ MSIS637 - Decision Support Systems

Summer 2007
MSTM623 - Decision Making Tools for the Technology Manager
MSTM800 - Global Aspects of Technology Management

Spring 2007
MSIS527 - Information & Systems Concepts in Organizations
MSIS637/IS452 - Decision Support Systems

Fall 2006
MSTM603 - Information & Systems Concepts in Organizations
MSIS638/IS438 -Information Systems Business Intelligence

Spring 2006
MSIS527 - Information & Systems Concepts in Organizations

Fall 2005
IS 408 - Data Management
IS452/MSCS637 - Decision Support Systems
IS 130- Information Systems Concepts

Spring 2005
MSIS 638 / IS 438 - Inf. Syst. Business Intelligence
IS 130- Information Systems Concepts
CMSC 408 / MSCS 542 - Database Mgmt Systems

Fall 2004
IS 408 - Data Management
IS452/MSCS637 - Decision Support Systems

Spring 2004
MSCS 591 / IS 471 - Inf. Syst. Business Intelligence
IS 130 - Information Systems Concepts
CMSC 408 / MSCS 542 - Database Mgmt Systems

Fall 2003
IS 408 - Data Management
IS 404 - Systems Analysis Methods
IS452/MSCS637 - Decision Support Systems

Spring 2003
IS 130 - Information Systems Concepts
CMSC 408 / MSCS 542 - Database Mgmt Systems

Fall 2002
IS 408 - Data Management
IS 404 - Systems Analysis Methods
IS 409 - Laboratory for Data Management (Oracle 8i)

Eitel J.M. Lauría