Artificial Intelligence (CSL 302) Spring 2014
Timings and Lecture Hall
L2 Lecture Hall, Monday - 3.00-3.50pm, Tuesday - 9.00-9.50am, Friday 9.00-9.50am
Description:The purpose of this course is to introduce fundamental concepts in artificial intelligence (AI) and provide a hands on experience with some of the classic AI techniques.
Prerequisites: Data Structures (CSL 201)
Textbook: Stuart Russell and Peter Norvig, Artificial Intelligence - A Modern Approach, Third Edition, Prentice Hall 2009 .
Grading: Homeworks-4(20%), Projects-3(45%), Exams-2(30%), Class Participation (5%)
Course Details: PDF
Instructor: Narayanan C Krishnan (CK)
Office Hours: Tuesday and Friday 10.00-11.00am or by appointment.
Office: 318
Phone: +91 1881 242273
Email: ckn@iitrpr.ac.in
Teaching Assistant: Yayati Gupta
Office Hours: Monday and Tuesday 11.00am-12.00pm or by appointment.
Office: 120
Email: yayati.gupta@iitrpr.ac.in
Grades: PDF
Homework:
Homework 1 is due on Jan 20, 2014.
Homework 2 is due on Feb 24, 2014.
Homework 3 is due on Apr 03, 2014.
Homework 4 is due on Apr 22, 2014.
Projects:
Project 1 is due on Feb 10, 2014.
Project 2 - totally three phases
- Phase 1 is due on Feb 28, 2014
- Phase 2 is due on March 10, 2014
- Phase 3 is due on March 19, 2014
Project 3 is due on Apr 16, 2014.
- Test data for MLP
Course Schedule - Lectures and Deadlines
Week |
Date |
Topic | Readings | Submission Deadlines |
|---|---|---|---|---|
1 |
Jan 7 |
Introduction | Chapter 1 | |
Jan 10 |
Introduction | Chapter 1 | ||
2 |
Jan 13 |
Intelligent Agents | Chapter 2 | |
Jan 14 |
Milad-Un-Nabi Holiday | |||
Jan 17 |
Intelligent Agents | Chapter 2 | ||
3 |
Jan 20 |
Uninformed Search | Chapter 3 | HW 1 PDF |
Jan 21 |
Uninformed Search | Chapter 3 | ||
Jan 24 |
Uninformed Search | Chapter 3 | ||
4 |
Jan 27 |
Informed Search | Chapter 4 | |
Jan 28 |
Informed Search | Chapter 4 | ||
Jan 31 |
Informed Search | Chapter 4 | ||
5 |
Feb 3 |
Local Search | Chapter 4 | |
Feb 4 |
Adversarial Search | Chapter 5 | ||
Feb 7 |
Adversarial Search | Chapter 5 | ||
6 |
Feb 10 |
Adversarial Search | Chapter 5 | Project 1 ZIP |
Feb 11 |
Constraint Statisfaction Problems | Chapter 6 | ||
Feb 14 |
Constraint Statisfaction Problems | Chapter 6 | ||
7 |
Feb 17 |
Logical Agents | Chapter 7 | |
Feb 18 |
Propositional Logic | Chapter 7 | ||
Feb 21 |
Propositional Logic | Chapter 7 | ||
8 |
Feb 24 |
Review midsemester exam topics | HW 2 PDF | |
Feb 25 |
Study Class | |||
Feb 28 |
Mid Semester Exams - No Class | Project 2 Phase 1 PDF | ||
9 |
Mar 3 |
First Order Predicate Logic | Chapter 8 | |
Mar 4 |
Mid Semester Exam Solutions Discussion | |||
Mar 7 |
Mid Semester Bonus Exam | |||
10 |
Mar 10 |
First Order Predicate Logic | Chapter 9 | Project 2 Phase 2 PDF |
Mar 11 |
First Order Predicate Logic | Chapter 9 | ||
Mar 14 |
Machine Learning | Chapter 18 | ||
11 |
Mar 17 |
(Holi)day | ||
Mar 18 |
Decision Trees | Chapter 18 | Project 2 Phase 3 PDF | |
Mar 21 |
Decision Trees | Chapter 18 | ||
12 |
Mar 24 |
Multilayer Perceptron | Chapter 18 | |
Mar 25 |
Regression | Chapter 18 | Guest Lecture | |
Mar 28 |
Multilayer Perceptron | Chapter 18 | ||
13 |
Mar 31 |
Evaluation and Model Selection | Chapter 18 | |
Apr 1 |
Support Vector Machines | Chapter 18 | HW 3 (due on April 3) PDF | |
Apr 4 |
Quantifying Uncertainty | Chapter 13 | ||
14 |
Apr 7 |
Probabilistic Reasoning | Chapter 14 | |
Apr 8 |
Ram Navami Holiday | |||
Apr 11 |
Probabilistic Reasoning | Chapter 14 | ||
Apr 12 |
Learning Probabilistic Models | Chapter 18 | ||
15 |
Apr 14 |
Learning Probabilistic Models | Chapter 18 | Project 3 (due of April 16) ZIP |
Apr 15 |
Reinforcement Learning | Chapter 21 | ||
Apr 18 |
Good Friday Holiday | |||
16 |
Apr 21 |
Reinforcement Learning | Chapter 21 | |
Apr 22 |
Concluding Remarks | HW 4 PDF |
Resources
Artificial Intelligence
- Artificial Intelligence Foundations of Computational Agents
- Artificial Intelligence by Rich and Knight
- UCI Machine Learning Dataset Repository
- Analytics and Data Mining Resources