This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations.
The learning goals of the Knowledge-Based AI course are to develop an understanding of (1) the basic architectures, representations and techniques for building knowledge-based AI agents, and (2) issues and methods of knowledge-based AI. The main learning strategies are learning-by-example and learning-by-doing. Thus, the course puts a strong emphasis on homework assignments and programming projects. The course will cover three kinds of topics: core topics such as knowledge representation, planning, constraint satisfaction, case-based reasoning, knowledge revision, incremental concept learning, and explanation-based learning; common tasks such as classification, diagnosis, and design; and advanced topics such as analogical reasoning, visual reasoning, and meta-reasoning.
Senior capstone course for the decision and information sciences major. Collected knowledge from the DIS courses and application to significant problems of size and complexity. State-of-the-art research ideas and current business and industrial practices in information systems.
Modern project management techniques that are used by modern practicing professionals were covered. Particular attention was given to the management of technology based systems and projects in a business enterprise. The topics covered included: defining project scope, alignment of projects with enterprise strategy, managing project cost, time and risks using tools such as CPM/PERT, and measuring project performance.
Examined the process of developing, implementing and analyzing strategies for successfully marketing a variety of existing and potential products and services on the Internet. Special attention was devoted to the tools and techniques unique to the electronic media.
Practiced techniques and tools applicable to the analysis and design of computer-based information systems. Practiced system life cycle, requirements analysis, and logical design of databases and performance evaluation. Emphasis was placed on case studies. The semester project involved the design, analysis and implementation of an information system.
The fundamentals of database management systems (DBMS), data models, query processing, and data warehouses, and their application in the development of business information systems was covered. An important goal of this course was to understand the value of information resources and information management challenges within an organization.
A structured approach to business application development and programming was provided. Problem solving techniques, program design, and logic, were emphasized. Hands-on exercises in which the class participated in designing and developing cross-disciplinary business applications were included.
Course in probabilistic and statistical concepts including descriptive statistics, set-theoretic development of probability, the properties of discrete and continuous random variables, sampling theory, estimation, hypothesis testing, regression and decision theory and the application of these concepts to problem solving in business and the application of these concepts to problem solving in business and management.
A course in which teams reverse engineered the most prominent successes in business today through the use of common business analysis tools (Five Forces, PESTLE, SWOT, Value Chain, VRIO, etc.,) in order to develop an understanding of the environmental and sucess factors involved in driving an innovative idea to global success. Teams then exercised the concepts learned within product development and project management simulations.
