New York University Skopje

 

Faculty of Computer Science and Information Technology

 

 

Introduction to Machine Learning and Data Mining

 

Spring 2008

 

Instructors: Prof. Dr. Igor Trajkovski

Class Hours: Thursdays, 13:15h 15:30h

Classroom: Lab 2

Office Hours: Tuesdays, 13:00h 15:00h

E-mail address: Prof. Dr. Igor Trajkovski (trajkovski AT nyus DOT edu DOT mk)

Phone Number: +389 2 2034 636

 

Textbooks:

                    Machine Learning; Tom Mitchel; McGraw Hill; 1997; ISBN-13: 978-0070428072

                    Artificial Intelligence: A Modern Approach; Stuart Russell, Peter Norvig; Prentice Hall; 2 edition 2002; ISBN-13: 978-0137903955

                    Data Mining: Practical Machine Learning Tools and Techniques; Ian H. Witten, Eibe Frank; Elsevier; 2005; ISBN-13: 978-0120884070

                    Programming Collective Intelligence: Building Smart Web 2.0 Applications; Toby Segaran; O'Reilly; 2007; ISBN-13: 978-0596529321

 

 

 


Course Description and Related Requirements

 

vCourse Description

 

Humans are capable of tackling extremely difficult problems without the benefit of an a priori solution.

They learn from experience and can often transfer knowledge acquired to novel instances or even whole new tasks.

Are machines capable of similar problem solving prowess?

This course will be a hands-on introduction to the basics of machine learning.

We will study multiple machine learning models including decision trees, neural networks,

bayesian learning, instance-based learning, and genetic algorithms.

 

 

vCourse Objectives

 

After completing this course, students will be able to understand some of the issues and challenges facing at machine learning

(generalization, bias, overfitting, model selection, feature selection and learnability)

while being exposed to the pragmatics of implementing machine learning systems. 

 

vGrading

The grade depends on the following:

Class participation 10%

Home work 20%

Project Work 70%

 

 

 

THERE WILL BE NO MAKE UP EXAMS

 

Weekly Plan

Week

 

Theme

1

Introduction [Slides]

2

Concept Learning [Slides]

3

Decision Tree Learning [Slides]

4

Artificial Neural Networks [Slides]

5

Bayesian Learning [Slides]

6

Learning Sets of Rules [Slides]

7

Introduction to WEKA

8

Instance Based Learning [Slides]

9

Ensembles [Slides]

10

Text Categorization [Slides]

11

Clustering [Slides]

12

Final Exam

 

 

 

vGrading Scale

 

A

(4)

10

93-100

B

(3.2)

9

85-92

C

(2.4)

8

77-84

D

(1.6)

7

69-76

E

(0.8)

6

61-68

F

0

5

0-60