2014 Course Syllabus
Basic Course Information
Instructor: Dr.
Christoph F. Eick
office hours (573 PGH): MO 4-5p and WE 1-2p (through April 30)
e-mail: ceick@uh.edu
Teaching Assistant: Wellington Cabrera
office hours (575 PGH): MO 1-2p and MO 4-5p
Wellington's COSC 6342 Website
class meets: M/W 2:30-4p
cancelled classes: none
Makeup class:
Course Materials
Required Text:
Ethem Alpaydin, Introduction to Machine Learning, MIT Press, Second Edition,
2010.
Recommended Texts: Tom Mitchell, "Machine Learning", McGraw-Hill, 1997.
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
Some Important Dates for the Spring 2014 Semester
We., March 5: 45 minute review of course material
Mo., March 17: Midterm Exam
January 20, March 10, March 12: No lecture
Mo., April 21 and We., April 23: Student presentations Project2
Mo., April 28: last lecture
Mo., April 28: 45 minute review of course material
Mo., May 5, 2p: Final Exam
Course Elements and their Weights
Due to the more theoretical nature of machine learning there will
be a little more emphasis on exams and on understanding the course material
presented in the lecture and textbook. However, there will be 2-3
projects and 2-3 graded homeworks (typically containing 15-20 problems total) which count about 40%
towards the overall grade; 3-4 of these activities will be group
projects / group homeworks. In 2014 the weights of the different
parts of the course are as follows:
Midterm Exam 26%
Final Exam 32%
Attendance 1%
Project1: 20%
Project2: 13%
Homeworks: 8%
2014 Course Projects, Reviews and Homeworks
Project1: Q-Learning for a Pickup
Dropoff World (
PD-World)
Homework1 (Group Homework; usually 3 students)
Homework Groups:
Group1:
Aleti,Sampath Vinayak
Barman,Arko
Bector,Shiwani
Group2:
Cao,Can
Chen,Juan
Chilluveru,Sri Lakshmi
Group3:
Dhawad,Priyanka
Ghosh,Sayan
Gopisetti,Narayana Jagdeesh
Group4:
Gopisetti,Satya Deepthi
Khalil,Nacer
Kulkarni,Amita A
Group5:
Liu,Tzu Hua
Majeti,Dinesh
Matusevich,David Sergio
Group6:
Memariani,Ali
Miao,Yang
Mirsharif,Seyyedeh Qazale
Group7:
MohammadMoradi,SeyyedHessamAldin
Mughal,Zakariyya Saeed
Nandamuri,Anil Kumar
Group8:
Nguyen,Toan Bao
Pavuluri,Bhagyasri
Ponnada,Venkata Sai Karthik
Group9:
Ravipati,Prudhvi
Rayanapati,Krishna Swathi
Sant,Prajakta Avinash
Group10:
Siebeneich,Eric J.
Uppaluri,Chaitanya
Wei,Li
Group11:
Wu,Yuhang
Xing,Yutao
Yang,Yajun
Zhang,Xiaolu
Review 1 March 5, 2014 (contains solutions to
the 2013 Homework1 and a few other problems from exams and other homeworks;
also briefly discusses some Solution
Sketches of the 2013 Midterm Exam)
Homework2&3 (final draft;
same groups as homework1; due April 15, 11p)
Project2: Making Sense of Data-Apply Machine Learning to an Interesting Dataset/Problem (Group Project; Discussion Project2 2014,
More on Project2)
Project2 Groups:
Group A:
Aleti,Sampath Vinayak
Gopisetti,Satya Deepthi
Khalil,Nacer
Kulkarni,Amita A
Group B:
Barman,Arko
Bector,Shiwani
Chilluveru,Sri Lakshmi
Nguyen,Toan Bao
Group C:
Cao,Can
Chen,Juan
Pavuluri,Bhagyasri
Siebeneich,Eric J.
Group D:
Dhawad,Priyanka
Ghosh,Sayan
Gopisetti,Narayana Jagdeesh
Group E:
Liu,Tzu Hua
Majeti,Dinesh
Matusevich,David Sergio
Group F:
Memariani,Ali
Miao,Yang
Mirsharif,Seyyedeh Qazale
MohammadMoradi,SeyyedHessamAldin
Group G:
Mughal,Zakariyya Saeed
Nandamuri,Anil Kumar
Uppaluri,Chaitanya
Wei,Li
Group H:
Ponnada,Venkata Sai Karthik
Ravipati,Prudhvi
Rayanapati,Krishna Swathi
Sant,Prajakta Avinash
Group I:
Wu,Yuhang
Xing,Yutao
Yang,Yajun
Zhang,Xiaolu
Review2 on Monday, April 28, 2014
(Exam3 in 2009,
2013 Machine Learning Final Exam,
Solution Sketches 2011 Homework3)
Course Topics
Topic 1: Introduction to Machine Learning
Topic 2: Supervised Learning
Topic 3: Bayesian Decision Theory (excluding Belief Networks)
Topic 5: Parametric Model Estimation
Topic 6: Dimensionality Reduction Centering on PCA
Topic 7: Clustering1: Mixture Models, K-Means and EM
Topic 8: Non-Parametric Methods Centering on kNN and density estimation
Topic 9: Clustering2: Density-based Approaches
Topic 10 Decision Trees
Topic 11: Comparing Classifiers
Topic 12: Combining Multiple Learners
Topic 13: Support Vector Machines
Topic 14: More on Kernel Methods
Topic 15: Naive Bayes' and Belief Networks
Topic 16: Neural Networks
Topic 17: Active Learning
Topic 18: Reinforcement Learning
Topic 19: Hidden Markov Models
Topic 20: Computational Learning Theory
Topics covered and order of Topic Coverage in 2014: Topic1, Topic18, Topic2, Topic3,
Topic5, Topic6, Topic7, Topic8, Topic10, Topic12, Topic11, Topic15, Topic19, Topic13,
Topic14, and Topic20.
Prerequisites
The course is mostly self-contained. However, students taking the course
should have
sound software development skills, and some basic knowledge of
statistics.
2014 Transparencies and Other Teaching Material
Course Organization ML Spring 2014
Topic 1: Introduction to Machine Learning(Eick/Alpaydin
Introduction, Tom Mitchell's Introduction
to ML---only slides 1-8 and 15-16 will be used)
Topic 2: Supervised Learning
(examples of classification techniques: Decision
Trees, k-NN)
Topic 3: Bayesian Decision Theory (excluding Belief Networks) and Naive Bayes (Eick on Naive Bayes)
Topic 4: Using Curve Fitting as an Example to Discuss Major Issues in ML (read
Bishop Chapter1 in conjuction with this material; not covered
in 2011)
Topic 5: Parametric Model Estimation
Topic 6: Dimensionality Reduction Centering on PCA
(PCA Tutorial, Arindam
Banerjee's More Formal Discussion of the Objectives of
Dimensionality Reduction)
Topic 7: Clustering1: Mixture Models, K-Means and EM
(Introduction to Clustering, Modified Alpaydin transparencies,
Top 10 Data Mining Algorithms paper)
Topic 8: Non-Parametric Methods Centering on kNN and Density
Estimation(kNN, Non-Parametric
Density Estimation, Summary Non-Parametric
Density Estimation, Editing and
Condensing Techniques to Enhance kNN, Toussant's survey paper on
editing, condesing and proximity graphs)
Topic 9: Clustering 2: Density-based Clustering
(DBSCAN paper,
DENCLUE2 paper)
Topic 10: Decision Trees
Topic 11: Comparing Classifiers
Topic 12: Ensembles: Combining Multiple Learners
for Better Accuracy
Topic 13: Support Vector Machines (Eick: Introduction
to Support Vector Machines, Alpaydin on
Support Vectors and the Use of Support Vector Machines for
Regression, PCA, and Outlier Detection (only transperencies which
carry the word "cover" will be discussed),
Smola/Schoelkopf Tutorial on Support Vector
Regression)
Topic 14: More on Kernel Methods(Arindam
Banerjee on Kernels,
Nuno Vasconcelos Kernel Lecture,
Bishop on Kernels; only transparencies 13-25 and
30-35 of the excellent Vasconcelos(
Homepage) slides 13-19, 22-24, 30-35
will be covered in 2013)
Topic 15: Naive Bayes and Belief Networks(Eick on Naive Bayes,
Eick on Belief Networks (used in the lecture),
Bishop on Belief Networks (not used in
the lecture, but might be useful for preparing
for the final exam)
Topic 16: Successful Application of Machine Learning
Topic 17: Active Learning (might be covered in 2014, if enough time)
Topic 18: Reinforcement Learning (Alpaydin on RL
(not used),
Eick on RL---try to understand those
transparencies; Using Reinforcement
Learning for Robot Soccer,
Sutton "Ideas of
RL" Video (to be shown and discussed in part in 2013),
Kaelbling's RL Survey Article---read
sections 1, 2, 3, 4.1, 4.2, 8.1 and 9 centering on what was
discussed in the lecture)
Topic 19: Brief Introduction to Hidden Markov Models
(HMM in BioInformatics)
Topic 20: Computational Learning Theory(Greiner
on PAC Learning,...)
Some old Exams: 2009 Exam2 Solution
Sketches, 2009 Exam3 Solution
Sketches, 2014 Midterm Exam Solution Sketches.
Remark: The teaching material will be extended and possibly
corrected during the course of the semester.
Grading
Each student has to
have a weighted average of 74.0 or higher in the
exams of the course in order to receive a grade of "B-" or better
for the course.
Students will be responsible for material covered in the
lectures and assigned in the readings. All homeworks and
project reports are due at the date specified.
No late submissions
will be accepted after
the due date. This policy will be strictly enforced.
Translation number to letter grades:
A:100-90 A-:90-86 B+:86-82 B:82-77 B-:77-74 C+:74-70
C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0
Only machine written solutions to homeworks and assignments
are accepted (the only exception to this point are figures and complex formulas) in the assignments.
Be aware of the fact that our
only source of information is what you have turned in. If we are not capable to understand your
solution, you will receive a low score.
Moreover, students should not throw away returned assignments or tests.
Students may discuss course material and homeworks, but must take special
care to discern the difference between collaborating in order to increase
understanding of course materials and collaborating on the homework /
course projects. We encourage students to help each other understand course
material to clarify the meaning of homework problems or to discuss
problem-solving strategies, but it is not permissible for one
student to help or be helped by another student in working through individual
homework problems and in individual course projects. If, in discussing course materials
and problems, students believe that their like-mindedness from such discussions could be
construed as collaboration on their assignments, students must cite each
other, briefly explaining the extent of their collaboration. Any
assistance that is not given proper citation may be considered a violation
of the Honor Code, and might result in obtaining a grade of F
in the course, and in further prosecution.
2013 Course Projects and Homeworks
Project1 (Project Description Project1): Making Sense of Data---Apply Machine Learning to a Dataset (Group Project; typically 2 students per group)
Project 2: Read, Understand, Summarize and Review a Machine Learning Paper
(Project2
Post-Analysis; Individual Project Part1;
group project Part2; 6 paper candidates to review include:
C1, C2, C3, C4, C5 and C6.
Papers on Reviewing: RV1, RV2)
Project 3: Application and Evaluation
of Temporal Difference Learning (Individual Project;
RST-World)
Homework1 (Individual)
Homework2 (Group Homework)
Homework3 (Individual)
2011 Homeworks and Projects
Graded Homework1
Graded Homework2
Graded Homework3+4
Project1: Using Machine Learning to Make Money in Horse
Race Betting (HorseRaceExample,
Project1 Discussions,
Preference Learning Tutorial,
Wolverhampton Statistics)
Project2: Group Project---Exloring
a Subfield of Machine Learning (Project2
Group Presentation Schedule).
Project3: Application and Evaluation of Temporal Difference
Learning (Project
Description, RST-World)
Master Thesis and Dissertation Research in Data Mining and Machine Learning
If you plan to perform a dissertation or Master thesis project in the areas of
data mining or machine learning, I strongly recommend
to take the "Data Mining" course; moreover, I also suggest to take at least one, preferably two, of the following
courses: Pattern Classification (COSC 6343), Artificial
Intelligence (COSC 6368) or Machine Learning (COSC 6342). Furthermore, knowing
about evolutionary computing (COSC 6367) will be helpful, particularly
for designing novel data mining algorithms.
Moreover, having basic knowledge in data structures, software design, and databases is important when conducting
data mining projects; therefore, taking COSC 6320, COSC 6318 or COSC 6340 is a also good choice.
Moreover, taking a course that teaches high preformance computing is also
desirable, because most data mining algorithms are very resource intensive.
Because a lot of data mining projects have to deal with images, I
suggest to take at least one of the many
biomedical image processing courses that are offered in our curriculum. Finally, having some knowledge
in the following fields is a plus: software engineering, numerical optimization techniques, statistics, and data visualization. Also be aware of the fact that having sufficient background in the above listed areas is a prerequisite for consideration for a thesis or dissertation project in the area of data mining. I will not serve as your MS thesis or dissertation advisor, if you have do not have basic knowledge
in data mining, machine learning, statistics and related areas. Similarly, you
will not be hired as a RA for a
data mining project without having some background in data mining.
Machine Learning Resources
Soybean Grand Challenge: Make some money with Machine
Learning!
ICML 2013 (ICML is the #1 Machine
Learning Conference)
Carlos Guestrin's
2009 CMU Machine Learning Course
Andrew Ng's Stanford
Machine
Learning Course
Andrew Moore's Statistical Data Mining Tutorial
Christoph Bishop IET/CBS Turing Lecture
Alpaydin Textbook
Webpage
