Download COMP 472/6721: Introduction to Artificial Intelligence

Department of Computer Science and Software Engineering
Concordia University
COMP 472/6721: Introduction to Artificial Intelligence
Winter 2017
Course Outline
Instructor: Javad Sadri, Office: EV3.233, Email: [email protected], Phone#: Ext. 8743
Course schedule:
Lecture, Section NN, Thursdays, 17:45–20:15, H-420
Labs
Section NNNI
Thursdays, 20:25-22:25, Room H-811
Section NNNJ
Mondays, 17:45-19:45, Room H-917
Section NNNK
Thursdays, 20:25-22:25, Room H-819
Office hours: Tuesdays and Thursdays, @ 13:10 – 14:10 (or by appointment)
Course Web Page: Many resources for the course will be available on the course webpage, accessible
through the URL: https://users.encs.concordia.ca/~c472_4/. Students should regularly consult the course
webpage for up-to-date information on the course, including slides, handouts, important announcements,
assignments, etc.
Course Objectives: COMP 472 Artificial Intelligence (4 credits) Prerequisite: COMP 352 or COEN 352.
The purpose of the course is to provide a broad technical introduction into the core concepts of Artificial
Intelligence (AI). Topics include: logics and automated reasoning, state-space search, uninformed and
informed/heuristic search, game playing, planning, knowledge representation, natural language processing
and basic machine learning techniques. Lectures: three hours per week, Laboratory: two hours per week.
Recommended Textbook: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach,
Pearson Higher Education, 2010 (Link for the book)
I would describe the textbook as highly recommended, in that all material for the tests will come from the
slides and materials based on our textbook or other recommended texts. That being said, a textbook can
often be very useful since the notes/slides, by definition, are much more compact.
Evaluation Scheme:
Components
Assignments (3)
Midterm Exam
Group Assignment
Final Exam
%
3x 5=15
20
1x10=10
55
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Assignments (15%): There will be a total of 3 individual assignments with equal weights. The main
purpose behind these assignments is to provide you with good preparation for the exams. You are required
to submit all of the assignments electronically before their corresponding due dates. All reports and
solutions for all assignments must be submitted online using EAS (URL: https://fis.encs.concordia.ca/eas/).
Please note that all assignments will be placed on the website; no hard copies of the assignments will be
distributed in class. No resubmission or late submission of assignments will be accepted.
Team Assignments (10%): There will be a team assignment. Each group (team) consists of maximum 46 students. You are required to submit your team assignment electronically before the corresponding due
date. Each team will submit only one report/solution electronically. All reports and solutions for all
assignments must be submitted online using EAS (URL: https://fis.encs.concordia.ca/eas/).
Mid-term exam (20%): The exam is a closed-book exam, and will be conducted tentatively on Thursday,
March 2, 2017. The exam will cover material from lectures, textbook, and assignments. There is no
substitution for a missed midterm exam.
Final exam (55%): The exam is a closed-book exam. The final exam date will be set by the University
Administration. The exam will cover material from the entire semester, including lectures, textbook, and
assignments. There is no substitution for a missed final exam. Both midterm and final exams will include
multiple choice questions, questions with short answers, and questions with detailed answers.
Passing Criteria and Grading scheme: There is no fixed, a priori relationship between the numerical
percentage and the final letter grades for this course, and letter grade distribution will be based on
the average of the class which is B-. In order to pass the course, you must pass both exams (midterm and
final), regardless of your grade in the assignments and projects. Please note, there is no standard relationship
between percentages and letter grades assigned. The grading of the course will be done based on the relative
percentages assigned to the assignments and the exams. For reasons of fairness, we may choose to scale
up/down the marks in a particular exam, or assignment to ensure that all aspects of the course receive a fair
weight. Any such "fine-tuning" will be made known to you before the final grades are assessed. Finally,
there are no pre-set cut-off points for the final grades; the cut-off points will be decided based on an
assessment of difficulty level, class performance, fairness, and instructor’s wisdom from teaching and
grading in the past.
Submission format: All assignment-or assignment related submissions must be adequately archived in a
ZIP file using your ID(s) and last name(s) as file name. The submission must also contain your name(s) and
student ID(s), and must follow a structured format which will be explained in the first lecture. Use your
"official" name only - no abbreviations or nick names; capitalize the usual "last" name. Inappropriate
submissions will be heavily penalized. Only electronic submissions will be accepted. Students will have to
submit assignments (again only one copy per group) using the EAS system before their deadlines.
Assignments must be submitted in the right folder of the assignments. Assignments uploaded to an incorrect
folder will not be marked and will evaluate to a zero mark. No resubmission or late submission will be
accepted
Attendance: Students are encouraged to attend the lectures and are responsible for all the material
presented and discussed.
Academic integrity: Students are strongly encouraged to study in groups and discuss with each other.
However, copying is strictly prohibited and all assignments, and projects suspected to be copies would not
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receive any marks. Also, those who are found copying will face severe consequences. You always must
indicate the names of the students with whom you had discussions for your assignments (or your projects).
Academic honesty requires you to adhere to this policy. In addition, students should be aware and observe
the academic integrity & the University’s code of conduct (academic) as specified on the Undergraduate or
Graduate Calendar, especially the parts concerning cheating, plagiarism, and the possible consequence of
violating this code. For more details, check out:
http://www.concordia.ca/programs-and-courses/academic-integrity/.
Tentative Course Material and References:
The list below provides a summary of the material that will be covered during the course. Please note that
this list is not inclusive: I may occasionally cover materials that are not provided in this list. It is also
possible that topics maybe covered in a slightly different order than that described in this table. Please check
course webpage for any changes or more details of the lecture notes.
Material
Introduction to AI: Overview and History
State-Space Search: Uninformed & Heuristic Search
Adversarial Search: Mini-Max & Alpha-Beta Pruning
Stochastic Methods: Review of Probability Theory & Naïve Bayes
Classification
Machine Learning I: Introduction, Decision Trees, Evaluation &
Unsupervised Learning
Machine Learning I Cont’d
Machine Learning II: Neural networks , Genetics Algorithms
Chapter
1, 26
3, 4, 1.1
5.1-5.4
13, 22.2
18.1-18.4
18.7, 18.11,
4.1.4
Natural Language Processing: Traditional levels of Analysis 22.1-22.3, 23
(Lexical, syntax, Semantics,…) & n-gram models
Predicate Logics & Automated reasoning: Review of Predicate 7.1-7.5, 8, 9
Logics, MGU, Backward & Forward Channing, ResolutionRefutation
Knowledge Intensive Problem Solving, Intelligent Agents
12.5.1
Catch-Up and final Review
Important Lecture Guideline: Laptops are STRICTLY PROHIBITED in classroom during the lectures.
Other communications devices, such as cellular phones and text/video messaging devices are also
STRICTLY PROHIBITED. The usage of any of these materials during the class will result in you being
asked to immediately leave the class.
Graduate Attributes:
As part of either the Computer Science or Software Engineering program curriculum, the content of this
course includes material and exercises related to the teaching and evaluation of graduate attributes.
Graduate attributes are skills that have been identified by the Canadian Engineering Accreditation Board
(CEAB) and the Canadian Information Processing Society (CIPS) as being central to the formation of
Engineers, computer scientists and information technology professionals.
As such, the accreditation criteria for the Software Engineering and Computer Science programs dictate
that graduate attributes are taught and evaluated as part of the courses. The following is the list of graduate
attributes covered in this course, along with a description of how these attributes are incorporated in the
course.
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Knowledge-base: Demonstrated competence of logics and automated reasoning, state space search
(uninformed and informed/ heuristic search, adversarial search), natural language processing and basic
machine learning techniques.
Design: the project in this course is presented in an open-ended fashion, and its size and complexity is
such that it can be tackled individually or in a team of 2. The assignments provide a platform for
designing solutions for complex, open-ended engineering problems and to design systems, components
or processes that meet specified needs with appropriate attention to health and safety risks, applicable
standards, and economic, environmental, cultural and societal considerations. Use and compose
appropriate AI techniques to solve a variety of problems.
Use of engineering tools: Ability to determine appropriate programming languages and software
libraries to develop programs that put into practice the foundations of artificial intelligence as taught in
the lectures.
Individual and in a team work: work either individually or in a team of maximum two members on
assignments and project.
Communications skills: Proper code documentation of the project and final project report. Oral
presentation for the project deliveries and tournament between the projects.
Important note: In the event of extraordinary circumstances beyond the University’s control, the content
and/or evaluation scheme in this course is subject to change.
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