University of Waterloo
Term and Year of Offering: Spring 2012
Course Number and Title: CS786, Probabilistic Inference and
Machine Learning
Website:
http://www.cs.uwaterloo.ca/~ppoupart/teaching/cs786-spring12/cs786-spring12.html
Newsgroup: http://www.piazza.com/uwaterloo.ca/spring2012/cs786
| Instructor's Name |
Office Location |
Contact |
Office Hours |
| Pascal Poupart |
DC2514 |
ppoupart@cs.uwaterloo.ca |
TTh 11:30-12:30 |
Course Description:
The design of automated systems capable of accomplishing complicated
tasks
is at the heart of computer science. Abstractly, automated
systems can
be viewed as taking inputs and producing outputs towards the
realization of
some objectives. In practice, the design of systems that produce
the
best possible outputs can be quite challenging when the consequences of
the
outputs are uncertain and/or dependent on other systems, the
information provided
by the inputs is incomplete and/or noisy, there are multiple (possibly
competing)
objectives to satisfy, the system must adapt to its environment over
time,
etc. This course will focus on the principles of probabilistic
inference and machine learning. The modelling and algorithmic
techniques that will be
covered
are quite versatile and can be used to tackle a wide range of problems
in
many fields including robotics (e.g., mobile robot navigation),
computer systems (e.g., autonomic computing, query optimization),
human-computer
interaction (e.g., spoken dialog systems, user modelling),
natural language processing (e.g., topic
modeling, document clustering), bioinformatics
(e.g., gene sequencing, design of experiments), data mining (e.g.,
fraud detection, information retrieval).
Hence,
the course should be of interest to a wide audience.
Course Objectives:
At the end of the course, students should have the ability to:
- Model inference and machine learning problems
- Design inference and machine learning algorithms
Course Overview:
The topics we will cover include:
- Model Representations
- Directed graphical models: Bayesian networks
- Undirected graphical models: Markov networks (including deep
belief networks and Markov logic networks)
- Probabilistic inference techniques
- Variable elimination
- Weighted model counting
- Inference as optimization
- Sampling techniques
- MAP inference
- Machine Learning
- Parameter estimation
- Partially observed data
- Structure learning
Required text:
The textbook for CS786 is Probabilistic Graphical Models: Principles
and Techniques MIT Press,
by Koller and Friedman. This will be the main
reference for the course. A copy is currently on reserve
at the library. Readings in the textbook are
assigned
for most lectures in the course schedule.
Evaluation:
The grading scheme for the course is as follows:
- Course project (50%)
- Five programming assignments (8% each)
- Paper presentation (10%)
NB: For an audit mark, you need to submit the assignments.
Assignments
There will be five assignments. Each assignment must be done
individually (i.e., no team) and will consist entirely of programming
questions. More precisely, you will be asked to program some
algorithms for probabilistic inference and machine learning and to test
them on some datasets. You are free to program in the language of
your choice, as long as it is supported by Marmoset, which is an
automated system to compile, run and evaluate programs.
Tests
There is no midterm and no final exam.
Rules for Group Work:
Assignments and projects must be done individually. The paper
presentations can be done in groups
of up to 3 people.
Indication of how late submission of assignments and missed
assignments will be treated
On the due date of an assignment, programs should be
submitted via Marmoset. Late programs may be submitted
for half credit within 24 hours. Programs submitted more than 24
hours late will not be marked.
Indication of where students are to submit assignments and pick up
marked assignments
Assignments must be submitted electronically via Marmoset. All that is
returned is a mark based on the performance of the program. The
marks will be made available via Marmoset.
Academic Integrity: In order to maintain a culture of
academic integrity, members of the University of Waterloo community are
expected to promote honesty, trust, fairness, respect and
responsibility.
[Check www.uwaterloo.ca/academicintegrity/
for more information.]
Grievance: A student who believes that a decision affecting
some aspect of his/her university life has been unfair or unreasonable
may have grounds for initiating a grievance. Read Policy 70, Student
Petitions and Grievances, Section 4, www.adm.uwaterloo.ca/infosec/Policies/policy70.htm.
When
in
doubt please be certain to contact the department's
administrative assistant who will provide further assistance.
Discipline: A student is expected to know what constitutes
academic integrity [check www.uwaterloo.ca/academicintegrity/]
to
avoid
committing an academic offence, and to take responsibility for
his/her actions. A student who is unsure whether an action constitutes
an offence, or who needs help in learning how to avoid offences (e.g.,
plagiarism, cheating) or about 'rules' for group work/collaboration
should seek guidance from the course instructor, academic advisor, or
the undergraduate Associate Dean. For information on categories of
offences and types of penalties, students should refer to Policy 71,
Student Discipline, www.adm.uwaterloo.ca/infosec/Policies/policy71.htm.
For
typical
penalties check Guidelines for the Assessment of Penalties,
www.adm.uwaterloo.ca/infosec/guidelines/penaltyguidelines.htm.
Appeals: A decision made or penalty imposed under Policy 70
(Student Petitions and Grievances) (other than a petition) or Policy 71
(Student Discipline) may be appealed if there is a ground. A student
who believes he/she has a ground for an appeal should refer to Policy
72 (Student Appeals) www.adm.uwaterloo.ca/infosec/Policies/policy72.htm.
Note for Students with Disabilities: The Office for persons
with Disabilities (OPD), located in Needles Hall, Room 1132,
collaborates with all academic departments to arrange appropriate
accommodations for students with disabilities without compromising the
academic integrity of the curriculum. If you require academic
accommodations to lessen the impact of your disability, please register
with the OPD at the beginning of each academic term.