University of Waterloo
Term and Year of Offering: Fall 2013
Course Number and Title: CS886, Natural Language
Understanding
Website:
http://www.cs.uwaterloo.ca/~ppoupart/teaching/cs886-fall13/cs886-fall13.html
Discussion Forum: piazza.com/uwaterloo.ca/fall2013/cs8862
Instructor's Name |
Office Location |
Contact |
Office Hours |
Pascal Poupart |
DC2514 |
ppoupart@uwaterloo.ca |
Mon 10:00-12:00
|
Course Description:
With the rise of question answering systems
With the proliferation of sensors, organizations are now collecting
streams of data about all kinds of processes (e.g., physiological
measurements, financial transactions, energy consumption, text
messages, etc.). There is a need to process this data and to
make intelligent decisions with respect to this data in order to
optimize desired processes (e.g., assistive technologies, portfolio
management, energy optimization, dialog management, robotic control,
etc.). Hence, this course will cover the theory and practice
of sequential decision making. More precisely, we will focus
on Markov decision processes, which provide a general framework to
model and optimize a wide range of decision processes in health
informatics, robotics, computational finance, human computer
interaction, computational sustainability, operations research,
etc. Since the dynamics of a process are usually only
partially known at the time of making decisions, we will also cover
reinforcement learning which provides a framework to simultaneously
learn about a process while making decisions.
Course Objectives:
At the end of the course, students should have the ability to:
- Model sequential decision making tasks
- Design algorithms for automated decision making and
reinforcement learning
Course Overview:
The topics we will cover include:
- Reasoning under uncertainty
- Decision Theory
- Sequential Decision Making
- Markov decision processes
- Offline optimization techniques
- Online optimization techniques
- Partially observable domains
- Decentralized decision making
- Multi-agent systems
- Reinforcement Learning
- Model-based techniques
- Model-free techniques
- Non-parametric techniques
- Bayesian reinforcement learning
- Potential applications:
- Robotic control
- Dialog management
- Operations research
- Health informatics and assistive technologies
- Intelligent tutoring systems
- Computational finance
- Computational sustainability
Textbook:
There is no required textbook. However complementary
readings (optional) will be recommended in several references (see
course
schedule)
- [JM] Daniel Jurafsky and James H. Martin (2008, 2nd edition) Speech
and Language Processing: An Introduction to Natural Language
Processing, Computational linguistics and Speech Recognition
- [A] James Allen (1994, 2nd edition) Natural Language
Understanding
- [BKL] Steven Bird, Ewan Klein and Edward Loper (2009) Natural Language Processing
with Python
- [MRS] Christopher D. Manning, Prabhakar Raghavan and Hinrich
Schutze (2008, 2nd edition) Introduction to
Information Retrieval
NB: The textbooks by Bird, Klein and Loper [KBL] and Manning,
Raghavan and Schutz [MRS] can be accessed freely by following the
links above.
Evaluation:
The grading scheme for the course is as follows:
- Course project (50%)
- Three programming assignments (10% each)
- One paper presentation (20%) or two paper critiques (10% each)
NB: For an audit mark, you need to submit the assignments.
Assignments
There will be three assignments, each worth 10% of the final
mark. 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
natural language understanding and to test them on some datasets.
You will also present one paper or write two paper
critiques. Paper presentations can be done in teams of at
most two people where as paper critiques must be done individually
(i.e., no team). Paper critiques should be saved in pdf
format and submitted by email to the instructor.
Tests
There is no midterm and no final exam.
Rules for Group Work:
Programming assignments, paper critiques and the project must be
done individually. Paper presentations can be done in teams
of at most two 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
electronically. Late programs may be submitted for half credit
within 24 hours. Programs submitted more than 24 hours late will not
be marked.
Paper critiques must be submitted by email to the instructor in pdf
format. Late critiques may be submitted for half credit within
24 hours.
Indication of where students are to submit assignments and pick
up marked assignments
Assignments must be submitted electronically. Marked
assignments will be returned electronically.
Paper critiques must be submitted by email in pdf format.
Marked critiques will be returned by email.
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.