CSL 452 - Artificial Intelligence - Spring 2016
Course Information
Timings and Lecture Hall
Monday 9.00-9.50am
Tuesday 9.55-10.45am
Wednesday 10.50-11.40am
Location L4
Lab Hours Thursday 9.00-10.45am
Description
Artificial Intelligence (AI) in an important area of Computer Science. AI is a well studied subject with utility in many real-world applications. This introductory course discusses some of the basic and widely used AI techniques, covering a wide range of topics such as search, AI for games, logic, planning, and reasoning. For a comprehensive list of topics covered in the course and course schedule, please see the course calendar. Practical experience will be gained through implementing the AI algorithms for different applications in Python/C/C++. For more details on lab assignments, please see the Labs webpage. This course has a pre-requisite of CSL201 (Data Structures)
Reference Material
Primary textbook - Stuart Russell and Peter Norvig, Artificial Intelligence - A Modern Approach, Third Edition, Prentice Hall 2009
Other reference books
- Artificial Intelligence by Rich and Knight
Instructor Details
Narayanan (CK) Chatapuram Krishnan
Office Hours: appointments through email
Office: 318
Phone: +91 1881 242273
Email: ckn@iitrpr.ac.in
Teaching Assistants Details
Shipra Sharma
Office Hours: Tuesday 2.30pm-5.00pm
Office: 120
Email: shipra.sharma@iitrpr.ac.in
Academic Integrity
It is expected that students who are taking this course will demonstrate a keen interest in learning and not mere fulfilling the requirement towards their degree. Discussions that help the student understand a concept or a problem is encouraged. However, each student must turn in original work. Plagiarism/copying of any form, will be dealt with strict disciplinary action. This involves, copying from the internet, textbooks and any other material for which you do not own the copyright. Copying part of the code will be considered plagiarism. Lending the code to others will be considered plagiarism too, for it is difficult to investigate who copied whose code. Students who violate this policy will directly receive a failing grade in the course. Remember - Your partial submission can fetch you some points, but submitting other's work as your own can result in you failing the course. Please talk to the instructor if you have questions about this policy. All academic integrity issues will be handled in accordance with institute regulations.
Grading Policy
Grading Policy
Quizzes: There will be approximately 7-8 pre-announced quizzes during the semester. Check the course calendar to learn about dates on which a quiz will be held. The top 6 quiz scores will count towards the student's overall grade. The quizzes will account for 30% of the overall grade. The quizzes will be held on almost every Thursday during the first hour of the lab.
Labs: There will be approximately 5 labs. Each lab will have a major programming component and will span for approximately two-three weeks. All the 5 labs will account for 20% of the overall grade. Students having difficulty with the labs are encouraged to contact the TA for assistance. You are not required to be physically present in the lab during the lab hours. You can complete the labs at your convenience and turn it in by the deadline.
Exams: The mid and end semester exams together will account for 50% (25% each) of the overall grade.
Attendance: There is no mandatory attendance. However attendance will be taken in every class. This will consitute a bonus of 1% for the final grade and might be helpful for all border line students.
Passing Critera: A student must secure an overall score of 40 (out of 100) and a combined score of 60 (out of 200) in the exams to pass the course.
Tentative Grade Breakup*
| Quizzes (6 out of 8) | 30% |
|---|---|
| Labs | 20% |
| Mid-Semester Exam | 25% |
| End-Semester Exam | 25% |
| Total | 100 |
*This is a tentative breakup of the grades and can change at the discretion of the instructor. However, any change with respect to the grade break-up will be intimated in advance.
Grade Sheet:PDF
Lectures and Calendar
Tentative Schedule and List of Topics*
| Week | Dates |
Topic | Readings | Quiz/Lab |
|---|---|---|---|---|
| 1 | Jan5-Jan8 |
Introduction and Intelligent Agents | Chapter 1 and 2 | |
| 2 | Jan11-Jan15 |
Uninformed Search | Chapter 3(3.1-3.4) | Q1 (14/1) |
| 3 | Jan18-Jan22 |
Informed Search | Chapter 3(3.5-3.6) | Q2 (21/1) |
| 4 | Jan25-Jan29 |
Local Search | Chapter 4(4.1-4.2) | L1 (29/1) |
| 5 | Feb1-Feb5 |
Adversarial Search, minimax, alpha-beta pruning, and game tree practice problems | Chapter 5(5.1-5.5) | Q3 (4/2) |
| 6 | Feb8-Feb12 |
Constraint Satisfaction Problems, practice problem | Chapter 6 | Q4 (11/2) |
| 7 | Feb15-Feb19 |
Logical Agents and Propositional Logic | Chapter 7(except 7.6.1) | L2 |
| 8 | Feb22-Feb26 |
First Order Predicate Logic | Chapter 8 (8.1-8.3) and Chapter 9(9.1-9.3, 9.4.1,9.5.1-9.5.3) | |
| 9 | Feb29-Mar4 |
Exam week Mid-Sem Sol Alpha-Beta Trace Q4.2 | ||
| 10 | Mar7-Mar11 |
Classical Planning | Chapter 10(10.1-10.2), Chapter 13 (Rich and Knight) | L3 ( |
| 11 | Mar14-Mar18 |
Classical Planning | Chapter 10(10.3) | Q5 (17/3) |
| 12 | Mar21-Mar25 |
Quantifying Uncertainty | Chapter 13 | Q6 ( |
| 13 | Mar28-Apr1 |
Probabilistic Reasoning I | Chapter 14(14.1,14.2) | L4 ( |
| 14 | Apr4-Apr8 |
Probabilistic Reasoning II | Chapter 14(14.3-14.5) | Q7 (7/4) |
| 15 | Apr11-Apr15 |
Utility Theory | Chapter 16(16.1-16.3) | Q8 ( |
| 16 | Apr18-Apr22 |
Markov Decision Process | Chapter 17(17.1-17.4) | L5 (22/4) |
| 17 | Apr25-Apr29 |
Reinforcement Learning | L6 (27/4) |
|
| 18 | May2-May6 |
Exam week End-Sem Sol |
*This is a tentative list of topics that will be covered during the semester. The topics and schedule can change according to the need at the discretion of the instructor.
Labs
- Simple Treasure Hunt -Lab 1 Due on 29th Jan 11.55pm
- Coal Block Auction and Three Musketeers Game - Lab 2 Due on
19th Feb 11.55pm23 Feb 11.55pm - CSP and Boolean SAT solver for Sudoku - Lab 3 Due on
11 March 11.55pm18 March 11.55pm - Block World Planner - Lab 4 Due on
1st April 11.55pm4 April 2016 - Bayesian Networks - Lab 5 Due on 22 April 11.55pm
- Mylab - Lab 6 due on 27 April 11.55pm