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Course Code and Name
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ROE 501-
Applied
Machine Learning
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Instructor
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Associate Professor Aytuğ ONAN
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E-mail
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aytug.onan@cbu.edu.tr
aytugonan@gmail.com
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Course Day and Time
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Tuesday, 15.00-17.00
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Course Website
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http://aytugonan.cbu.edu.tr/ROE501_index.html
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Objectives
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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.
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Tentative Course Outline
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Week#1: Course
Introduction
Week#2: Introduction
to Machine Learning
Week#3: Loss
Functions
Week#4: Neural
Networks
Week#5: Neural
Language Models (Project Proposal!!)
Week#6: Recurrent
Neural Networks
Week#7: No
Class (Midterm Week!!)
Week#8: LSTM
Week#9: Sequence
to Sequence Models
Week#10: Convolutional
Neural Networks
Week#11: Domain
Adaptation
Week#12: Student
Presentations
Week#13: Student
Presentations
Week#14: Student
Presentations
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Textbook
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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.
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Supplementary Materials
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Kuncheva, L. I. (2004). Combining pattern
classifiers: methods and algorithms. John Wiley & Sons.
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Evaluation
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Research Project:
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Research Presentation: 30% (Week#11-14)
Final Report: 70% (Week#11)
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This will be a project involving
choosing an interesting machine learning question, finding relevant
data, using an
appropriate toolbox (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.
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