ROE 505--- Artificial Intelligence

Izmir Katip Çelebi University, Graduate School of Natural Sciences

Fall 2019

Syllabus

Course Code and Name

ROE 505- Artificial Intelligence

Term

Fall 2018-2019

Instructor

Associate Professor Aytuğ ONAN

E-mail

aytug.onan@cbu.edu.tr

aytugonan@gmail.com

Course Day and Time

Thursday, 13.30-16.00

Course Website

http://aytugonan.cbu.edu.tr/ROE505_index.html

Objectives

In the modern IT world, businesses often have access to large amounts of data collected from customer management systems, web services, customer interaction, etc. The data in itself does not bring value to the business; we must bring meaning to the data to create value. Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data. This course will focus on applied machine learning, where we learn what algorithms and approaches to apply on different types of data.

Tentative Course Outline

 

Week#1: Course Introduction

Week#2: Introduction to Artificial Intelligence

Week#3: Data and Learning

Week#4: Text Classification

Week#5: Numerical Regression

Week#6: Decision Support

Week#7: Kernel Methods and Support Vector Machines

Week#8: Midterm Week

Week#9: Artificial Neural Networks

Week#10: Deep Learning

Week#11: Ensemble Learning

Week#12: Genetic Algorithms

Week#13: Student Presentations

Week#14: Student Presentations

Week#15: Student Presentations

Textbook

Mining of Massive Datasets
Leskovec, Jure & Rajaraman, Anand & Ullman, Jeffrey David (2014), Cambridge University Press, 476 pages.
Available for free online here.

Deep Learning
Goodfellow, Ian & Bengio, Yoshua & Courville, Aaron (2016), MIT Press, 781 pages.
Available for free online here.

Supplementary Materials

Kuncheva, L. I. (2004). Combining pattern classifiers: methods and algorithms. John Wiley & Sons.

Evaluation

Research Project:

Research Proposal: 10%             (Week#4)

Literature Survey: 25%              (Week#7)

Research Presentation: 15%      (Week#11-14)

Final Report: 50%                       (Week#11)

 

This will be a  project involving choosing an interesting machine learning question, finding relevant data, using an appropriate toolbox (Weka, Matlab, TensorFlow, R, Scikit, Keras, etc.) to answer the question, writing it up, and presenting it to the class. This should be done as an individual.