INDR 451/551 Machine Intelligence and
Data Analysis for Biology and Finance
Spring 2016
Course Syllabus
Instructor
Office Hours
Office Location
Office Phone
Email
Web Address
Mehmet Gönen
TuTh 13:00 – 14:15
ENG 118
1813
[email protected]
http://home.ku.edu.tr/~mehmetgonen
Class Meeting Location ENG Z27
Class Meeting Times TuTh 16:00 – 17:15
Number of Credits
ETC Credits
Language
Course Web Page
3
5
English
http://home.ku.edu.tr/~mehmetgonen/indr451_551_spring2016.html
Required Textbook:
None.
Supplementary Textbooks:
•
•
•
•
Introduction to Machine Learning by Ethem Alpaydın.
Pattern Recognition and Machine Learning by Christopher M. Bishop.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy.
Bayesian Reasoning and Machine Learning by David Barber.
Required Software:
•
•
R (https://cran.fhcrc.org)
RStudio (https://www.rstudio.com/products/rstudio/download/).
Grading System:
Discussion/Participation
Project Code
Project Presentation
Project Report
Final
10%
20%
20%
20%
30%
Course Description:
This course provides a broad introduction to machine intelligence and data analysis together with
applications in biology and finance. Topics that will be covered include clustering, regression,
classification, and dimensionality reduction. Students will have hands-on experience on real-life
biology and finance problems such as molecular subtyping of cancer patients, customer
segmentation, drug sensitivity prediction, stock price forecasting, identification of benign and
malignant tumors, bankruptcy prediction, visualization of high-dimensional biological/financial
data sets.
Course Objectives:
The students will be taught different machine intelligence and data analysis algorithms. On
completion of this course, the students should be able to solve real life problems using the battery
of algorithms they learned.
Learning Outcomes:
•
•
•
•
•
•
•
Identify potential applications of machine intelligence and data analysis in biology and
finance.
Describe the core differences in analyses enabled by clustering, regression, classification,
and dimensionality reduction algorithms.
Select the appropriate algorithms for a potential application.
Apply clustering, regression, classification, and dimensionality reduction.
Represent your data as features to serve as input to algorithms.
Assess the model quality in terms of relevant error metrics for each task.
Utilize a data set to fit a model to analyze new data.
Course Honor Principle:
Misconduct during the classes cannot be tolerated and will require disciplinary action according
to Koç University policies. We expect and encourage students to discuss readings, computer
exercises, and other course content with their classmates. However, all work counted towards the
students' grade, including the homework exercises, case reports, and examinations must be
prepared/answered solely by the individual student or the group members submitting it.
In addition, students are expected to prepare homework and other instructional materials without
using materials or advice from students who have taken the course previously. You will get a grade
of zero from an assignment or a project report even if only a portion of it is evidently same as that
of another group.
Cheating in any form will not be tolerated during the examinations. Any student caught cheating
will be censured in full accordance with Koç University policies. Cheating, plagiarism, and
collusion are serious offenses resulting in an F grade and disciplinary action. Please refer to the
Koç University academic rules and regulations, the Student Code of Conduct, and the Classroom
Code of Conduct on KU web page for an explicit statement of what constitutes plagiarism, cheating
or collusion.
Academic dishonesty in the form of cheating, plagiarism, or collusion are serious offenses and are
not tolerated at Koç University. University Academic Regulations and the Regulations for Student
Disciplinary Matters clearly define the policy and the disciplinary action to be taken in case of
academic dishonesty. Failure in academic integrity may lead to suspension and expulsion from the
University.
Cheating includes, but is not limited to, copying from a classmate or providing answers or
information, either written or oral, to others. Plagiarism is borrowing or using someone else’s
writing or ideas without giving written acknowledgment to the author. This includes copying from
a fellow student's paper or from a text (whether printed or electronic) without properly citing the
source. Collusion is getting unauthorized help from another person or having someone else write
a paper or assignment.
Detailed Course Outline:
Week
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Date
Feb 2
Feb 4
Feb 9
Feb 11
Feb 16
Feb 18
Feb 23
Feb 25
Mar 1
Mar 3
Mar 8
Mar 10
Mar 15
Mar 17
Mar 22
Mar 24
Mar 29
Mar 31
Apr 5
Apr 7
Apr 12
Apr 14
Apr 19
Apr 21
Apr 26
Apr 28
May 3
May 5
May 10
May 12
Location
ENG B32
ENG B32
ENG B32
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
No lecture
No lecture
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
ENG Z27
Topic
Course Overview
Introduction to Machine Intelligence and Data Analysis
Clustering Algorithms
Clustering Algorithms
Clustering Applications in Biology
Clustering Applications in Biology
Clustering Applications in Finance
Clustering Applications in Finance
Regression Algorithms
Regression Algorithms
Regression Applications in Biology
Regression Applications in Biology
Regression Applications in Finance
Regression Applications in Finance
Classification Algorithms
Classification Algorithms
Classification Applications in Biology
Classification Applications in Biology
Classification Applications in Finance
Classification Applications in Finance
Holiday
Holiday
Dimensionality Reduction Algorithms
Dimensionality Reduction Algorithms
Dimensionality Reduction Applications in Biology
Dimensionality Reduction Applications in Finance
Project Presentations
Project Presentations
Project Presentations
Project Presentations