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Most of the work in this course will be team-based and task-oriented. Course Objectives : Students will be able to acquire, store, access, visualize, and analyze big data.

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Get familiar with the structure of Hadoop then learn the analytic aspect of big data as the main focus of the course such as clustering and classification, decision trees and random forests, and dimensionality reduction. Course Description : This course provides an in-depth study of advanced data mining, data analysis, and pattern recognition concepts and algorithms. Course content builds upon a first data mining course and explores advanced machine learning algorithms, high-dimensional data and temporal data, and advanced methods and applications to deal with dynamic stream data. Various applications will be considered, including social networks and health informatics.

Course Objectives : Students will be able to understand the research methods applied in the field of data mining and knowledge discovery and conduct an end-to-end data mining project and document and present the results.

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They will be able to use the techniques covered in the course for building an end-to-end data mining project. You can find more detailed information on this course here.


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Graduate course In the context of distributed information processing fields of information systems and artificial intelligence have a substantial potential for converging. Fundamental common problem: autonomy of distributed information processing agents. Distributed information systems have to deal with the consequences of autonomy: heterogeneity, inconsistency, inefficiency.


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Artificial Intelligence has developed in the area of agents methods to deal with autonomy, planning, coordination, negotiation. In order to successfully take advantage of the convergence of the areas one needs to know the fundamentals of both of them. Topics of this course include inference engines, search algorithms and constraint satisfaction, scheduling, management of innovative projects, case-based reasoning, diagnosis with logic, probabilities and models.

This course provides the theoretical and practical background required to obtain the qualification for efficient team work in modern language and speech engineering, it includes lectures on speech signal processing, speech and speaker recognition, and natural language processing, introductions to language engineering applications, and linguistic training for language and speech engineers.

This course gives a firm foundation to decision theory from mainly a statistical and algorithmic, but also a philosophical perspective.

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The aims of the course are two-fold. Firstly, to give a thorough understanding of statistical decision theory, the meaning of hypothesis testing, automatic methods for designing and interpreting experiments and the relation of statistical decision making to human decision making. To be successful, students have to be at the forefront of this new technology. This means taking a "hands-on" approach to developing the requisite skills necessary to become "marketing engineers".

Expert Systems Lab Course

This course deals with concepts, methods, applications of decision modeling to address various marketing issues. Unlike conventional capstone business courses that focus on conceptual material this course will attempt to provide skills to translate conceptual understanding into developing specific operational models for improved decision-making - a skill in increasing demand in corporations today. The course will be of particular relevance to students planning careers in marketing and management consulting.

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The course is designed for students with some quantitative background as well as some exposure to marketing concepts. Lilien, Philip Kotler, and K. This is a reference book that supplements course materials and class discussions. This text will be useful for answering the more advanced questions. Each individual is expected to read and report on a book of their choice relating to this course.