COURSE INFORMATION Winter 2014
60-574 MACHINE LEARNING
School of Computer Science, University of Windsor
Instructor:
Dr. Alioune Ngom,
School of Computer Science.
Email: angom@cs.uwindsor.ca
Phone Extension: 3789
Room: 5107 LT
Office Hours: Thursday 08:30am to 12:00pm
PURPOSE OF THIS COURSE AND LEARNING RESOURCES:
Course Description and Objectives
Machine Learning (as well as Data Mining) has as its goal the automatic acquisition of knowledge from examples. Obtaining
concepts from examples is a typical learning task. The examples may be, e.g., news items selected by an internet user, and the
concept will be a description of user’s interests inferred from his selections. Or genetic expression knowledge can be ‘learned’
from time-series data of DNA chip results. A functional view of Machine Learning is that it performs data analysis and
extraction of knowledge from data. Many Machine Learning techniques are used in Data Mining.
The objective of this course is to provide an introduction to the basic principles, techniques, and applications of Machine
Learning. Programming assignments are used to help clarify basic learning concepts. The emphasis of the course is on teaching
the fundamentals, and not on providing a mastery of specific commercially available software tools or programming
environments.
This course is about the principles, design and implementation of learning agents --- programs that improve their performance
on some set of tasks with experience. A range of learning paradigms and methods will be covered, such as: Concept and
Decision Tree Learning, Neural and Evolutionary Learning, Probabilistic and Statistical Learning, Inductive and Analytical
Learning, and Instance-Based and Reinforcement Learning.
Upon successful completion of the course, you will have the capability to develop learning algorithms or systems, to analyze
their behavior and to empirically and theoretically estimate their performance. You will have an understanding of a variety of
practically useful machine learning algorithms and their use in data-driven knowledge discovery and program synthesis. You
will have designed and implemented several machine learning algorithms. You should also be able to adapt or combine some
of the key elements of existing machine learning algorithms to design new algorithms as needed. The emphasis of the course is
on machine learning algorithms that are useful in practice. However, we will cover some of the key theoretical results that have
significant practical implications.
Lectures
Lecture Hours and Location: Tuesday 08:30am to 11:20am --- To be Announced
Students will have the opportunity to meet with the instructor and with other students through the following:
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Some formal lectures every week – see schedule for times.
One-to-one consultations with the instructor as required.
Discussion and interaction with other students.
Course notes, textbook and web page
Homepage: http://cs.uwindsor.ca/~angom
Recommended Textbook (no particular textbook is required):
 Tom M. Mitchell, Machine Learning, Mc-Graw-Hill, ISBN 0-07-042807-7, 1997
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Ethem Alpaydin, Introduction to Machine Learning, MIT Press, ISBN 0-262-01211-1, 2004
Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference,
and Prediction, Second Edition, Springer, ISBN 978-0-387-84857-0, 2009
Internet resources: Dr. Andrew Moore’s tutorials at http://www.autonlab.org/tutorials/, and, Dr. Ethem Alpaydin’s
slides at http://www.cmpe.boun.edu.tr/~ethem/i2ml/. And many others.
Software resources
WEKA can be downloaded from http://www.cs.waikato.ac.nz/~ml
Matlab, and other programming language compilers
Basic knowledge of Java, C or C++ is expected.
WORK TO BE UNDERTAKEN BY STUDENTS:
Preparation for lectures
Attendance at all lectures is highly recommended. Some of the concepts covered are difficult and
professors will attempt to present the concepts in such a way as to make them easier to understand.
Students should read the course notes and textbook ahead of lectures. A detailed schedule showing the topic
of each lecture is given later. Lectures are not substitutes for student reading. Students who do not read
ahead may find themselves lost in the lectures.
Evaluation Scheme
The final mark will consist of
1. Class Participation: 10%
2. Presentation: 15%
3. Assignments: 25%
4. Project: 50%
Students who wish to appeal a class-test, exam, or assignment mark should do so within a week of receiving the mark. If
disagreements between a student and the instructor persist, then the student should wait until s/he received his final
grade at the end of the semester and then follow the procedure outlined in the University Calendar for the appeal of
that grade. No remarking of class tests or the final exam will be undertaken unless a formal grade appeal is submitted
at the end of the semester after the student has received the final grade for the course. Numerical errors in adding
marks on class tests and the final exam will be corrected when identified. There are no make-up tests, so please do not
miss a test.
The University of Windsor uses a percentage marking and grading scale as of Fall 2013. Only raw % scores (e.g., 75%) are
assigned in course work and only raw scores are used in the computation of cumulative, major and sessional grade point
averages. The meaning of scores (or logical conversion of raw scores to grades) in transcripts are with a conversion scale
different from the one used previously. This new conversion scale can be found through www.uwindsor.ca/calendar, then
grading and marks/grades descriptors link. Only raw % scores are assigned in course work and the meaning of scores in
transcripts are:
 90
 85
 80
 77
 73
 70
 67
=100
< 90
< 85
< 80
< 77
< 73
< 70
A+
A
AB+
B
BC+
 63
 60
 57
 53
 50
0
<67
< 63
< 60
< 57
< 53
< 50
C
CD+
D
DF
In computing a student's average, grades from 0% to 22% are calculated as 22%. Grades from 23% to 40% calculated as 40%.
Grades from 40% to 49% are calculated as is into the student's average. All grades are recorded in the transcript as is. All
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grades below 50% are considered failures. (see mark/grades descriptor page of calendar www.uwindsor.ca/calendar for
details).
Teaching Evaluation
Student Evaluation of Teaching (SET) forms will be administered during the last two weeks of the class schedule.
POLICY ON CHEATING:
The professor will put a great deal of effort into helping students to understand and to learn the material in the course.
However, he will not tolerate any form of cheating.
The professor will report any suspicion of cheating to the Director of the School of Computer Science. If sufficient evidence is
available, the Director will begin a formal process according to the University Senate Bylaws. The instructor will not negotiate
with students who are accused of cheating but will pass all information to the Director of the School of Computer Science.
The following behavior will be regarded as cheating (together with other acts that would normally be regarded as cheating in
the broad sense of the term):
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
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Copying assignments
Allowing another student to copy an assignment
from you and present it as their own work
Copying from another student during a test or
exam
Referring to notes, textbooks, etc. during a test or
exam
Talking during a test or an exam
Not sitting at the pre-assigned seat during a test
or exam
Communicating with another student in any way
during a test or exam
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Having access to the exam/test paper prior to the
exam/test
Asking a teaching assistant for the answer to a
question during an exam/test
Presenting another’s work as your own
Modifying answers after they have been marked
Any other behaviour which attempts unfairly to
give you an advantage over other students in the
grade-assessment process
Refusing to obey the instructions of the officer in
charge of an examination.
Students who are found guilty of any form of cheating will be given a grade of F- for the whole course.
Several University of Windsor students have been caught cheating during the last few years. In most cases the evidence was
sufficient to invoke a disciplinary process which resulted in various forms of punishment including letters of censure, loss of
marks, failing grades, and expulsions. As example, a student who copied a project from another student and presented it as his
own was expelled from the university. In course, 60-100, a student who copied answers during a final exam received a failing
grade.
Do not cheat, if you are caught and found guilty, you could be thrown out of the university and will have to explain why when
you go looking for a job.
Tentative course lecture schedule:
Week
1
2
3
4
5
6
7
8
9
10
11
12
Lecture Topic
Introduction to Machine Learning
Concept Learning and the General-to-Specific Ordering
Decision Tree Learning
Artificial Neural Networks
Evaluating Hypothesis
Bayesian Learning
Computational Learning Theory
Instance-Based Learning
Genetic Algorithms
Learning Set of Rules
Analytical Learning
Grammatical Learning and Hidden Markov Models
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13
Reinforcement Learning