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If you have questions about this program, email the program leader listed below.

Introduction to Machine Learning & Pattern Recognition - Italy

The arrangements for the travel, housing, meals, excursions and course content of this program have been made by the leader. Questions about this program can only be answered by the program leader listed below.

Program Type Departmental
Program Location Rome, Italy
Course # SA 10503
Duration Summer
Upcoming Program Dates
Summer 2018 05/09/18 to 06/02/18

Program Leader

Name Okan Ersoy

Enrollment Details: Enrollment Currently Closed
Applications for this program have been closed.

Program Description

Please note that students will be in class at Purdue on May 7 and 8 PRIOR to departure for Rome.  These classes are mandatory.  

More program details can be found at the program's website.

Course Objective: To provide the student with the basic topics in machine learning and pattern recognition algorithms such as neural networks, support vector machines, decision trees, data mining and related methods for the design of intelligent and adaptive systems, to describe how they are used in applications, especially involving information and advanced technologies, and to provide hands-on experience with software tools. 

Course Description: Topics covered include intelligent information processing, search and retrieval, classification, recognition, prediction and optimization with machine learning and pattern recognition algorithms such as neural networks, support vector machines, decision trees and data mining methods, current models and architectures, implementational topics, applications in areas such as information processing, search and retrieval of internet data, signal/image processing, pattern recognition and classification, prediction, optimization, simulation, system identification, communications and control. Classification and recognition are very significant in a lot of domains such as multimedia, management, finance, radar, sonar, optical character recognition, speech recognition, vision, remote sensing, agriculture, bioinformatics and medicine. We will discuss how intelligent learning algorithms are used in these areas with a number of practical examples from real-world problems. Prediction is an application domain of classical significance. For example, predicting market prices in the near future is an interesting example. What types of signals are predictable? How do linear versus nonlinear prediction techniques compare? What are the best techniques for prediction? We will discuss answers to such significant and practical questions, with illustrations on a number of real-world problems. System identification is very important, for example, in order to optimize a company’s performance in a defined manner, such as optimization of productivity. For this purpose, it is necessary to do system modeling first. Then, the inputs can be optimized to generate the best output(s) possible from the system. This topic is closely related with system optimization, and techniques such as Six Sigma and Design of Experiments. Data mining is streamlining the transformation of masses of information into meaningful knowledge. It is a process that helps identify new opportunities by finding fundamental truths in apparently random data. The patterns revealed can shed light on application problems and assist in more useful, proactive decision making. Design of data mining systems using intelligent learning algorithms is an important topic of this course. Internet has become a major global mechanism for processing, search and retrieval of information and data, and led to new technologies such as e-commerce, e-business, web-based communications and networking. The algorithms learned in this course are fast becoming major tools for intelligent internet information processing and technology. As other examples of significant application areas of recent interest, bioinformatics, remote sensing and prediction of financial time series can be cited. In bioinformatics, the application may be DNA sequence analysis, drug design, and similar topics such as proteomics. In remote sensing, the application may be classification and modeling with multispectral, hyperspectral, radar, lidar and optical data. A major interest in finance and econometrics is to enhance the accuracy of prediction, for example, of financial time series, as well as to automate digital processing for real time prediction and search. Statistical and computational techniques to be discussed in this course have become very important in these and similar areas. The algorithms learned in this course are also very important to model and analyze global environmental applications, which are assuming more and more significance.

Examinations: Two hour examinations. Each hour exam will cover the material between the previous exam and the current exam.

Grade: 35% each exam, 30% homeworks and projects.

Textbook: Machine Learning, An Algorithmic Perspective by Stephen Marshall (Chapman&Hall/CRC Press, 2009, ISBN: 978-1-4200-6718-7) and the lecturer’s course notes

Academic Credit

Participants who complete the program will earn three credits of ECE 30010.  Please check with your academic advisor to see how it can be used in your plan of study. 



 Open to all engineering stiudents who have completed their sophomore year by the start of the program through seniors.  

Prerequisites: Math 261 or Math 265 (or equivalents) Prerequisite Topics: Calculus, introductory linear algebra ( probability and statistical concepts used will be introduced during lectures). Homeworks: including software exercises as miniprojects. 

Please note that students will be in class at Purdue on May 7 and 8 PRIOR to departure for Rome.  These classes are mandatory.   

Program Cost

Estimated cost is $3350.  Students eligible for the Purdue Moves Scholarship will receive a $1,000 scholarship toward the costs.  You must have a FAFSA on file to be eligible for the scholarship.  Check your eligibliy here  

Cost includes:  All housing, airport pick up in Rome, transportation pass, breakfast daily, Pizza and Gelato Crawl, Italian Cooking class, bike ride on the Appian Way, guided Rome city tour, Welcome and Farewell dinners, one guest lecture on the history of Rome, three credits and class fees, On-site health and safety orientation, and International Health Insurance.  

Additional costs:  Air ticket, meals, passport, Visa (if necessary), personal excursions and personal spending money.  

ECE students may be eligible for an additional scholarship after the successful completion of this program.  Contact Anne Tally in ECE for more information at 


Application Deadline

Applications will be accepted on a rolling basis until the program is full. Students must have paid a deposit by February 1st.

Country map courtesy of The General Libraries, The University of Texas at Austin