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Fall 10 - AI

Course Description: This course provides a generic introduction and explanation of the most prominent branches of the science of Artificial Intelligence (AI). One of the aims of this course is to introduce the undergrad students to the concept of devising and implementing research-based projects, i.e., those projects which have the potential to be presented as research work. Topics covered will include the operation of intelligent agents, intelligent search (solution-discovering) algorithms and constraint satisfaction problems, First-Order Logic and its inference, Knowledge Representation, Planning, Uncertainty and Bayesian Networks, and Machine Learning. For all these topics, students will be introduced to the current implementation techniques and research trends, in order to motivate them to develop these trends further.

Course Goals: This course aims to provide the students with a concrete grasp of the fundamentals of various branches that currently constitute the field of Artificial Intelligence, e.g., autonomous planning, autonomous control, data mining, natural language processing etc. It also aims to train you into devising and implementing a research-based AI Project. You will learn how to: 1) Capture the state-of-the-art of some AI branch, 2) Detect its limitations, and 3) Propose new solutions (and implement them). Important: The students should be able to generate enough novel and concrete solutions that would allow their work to be published in some conference/workshop. 

Core/Elective: This is an elective course.

Pre-requisites: Data Structures 

Administrative Info:

Instructor: Dr. Tariq Mahmood | Office Hours: TBA

Sections and Lectures: 2 Sections (A and B) with 6 lectures/week

Primary Textbook: Artificial Intelligence: A Modern Approach (2nd Edition), by Stuart Russell and Peter Norvig

Reference Textbooks: Artificial Intelligence: A Guide To Intelligent Systems (2nd Edition), by Michael Negnevitsky, and Agent Technology For Communication Infrastructures, by Alex L. G. Hayzelden and Rachel A. Bourne.

Course Outline: Attached Below

Grade Distribution: Quizzes – 5%, Assignments – 10%, Mid-Term 1 – 10%, Project – 15%, Mid-Term 2 – 10%, Final Exam – 50%

Assignments and Projects: The students would be required to divide themselves into groups. Both the assignments and the project are to be submitted collectively by the whole group. There would be three assignments, pertaining to the current contents that being taught in the course, with weightages of 3%, 3% and 4% respectively.

Cheating Policy: Simply put, any two or more matching assignments will be marked directly with a 0. No compromise or consultation would be permitted in this regard.

Attendance Policy: This is very strict. Absolutely no compromise would be made for those students who are not motivated or serious enough to attend classes. My own policy is that if you find the course uninteresting despite the efforts of the teachers, then you should drop the course, rather than hanging on and bunking classes.

Class Discipline Policy: Any student who is disrupting the environment of the class will simply be asked to leave. If this student persists with disruptive demeanor, then he/she will be permanently disallowed from attending any further classes. No compromise or complaints would be entertained in this regard. 


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