BITS WILP Artificial Intelligence Handout 2017-H2

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BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
WORK INTEGRATED LEARNING PROGRAMMES
Digital
Part A: Content Design
Course Title
Artificial Intelligence
Course No(s)

IS ZC444
Credit Units
3
Credit Model

Content Authors
Ramprasad Joshi

Course Objectives
No

CO1
To give student a flavor of classical AI
CO2
Build the foundation to designing Intelligent agents
CO3
Give a gentle start on Machine learning

Text Book(s)
T1
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig 3rd Edition (AIMA)

Reference Book(s) & other resources
R1
Video Lecturers:
https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x
R2
A.M. Turing(1950) Computing Machinery and Intelligence Mind LIX (236): 433-460
R3
Video Lecturers:
https://www.coursera.org/learn/machine-learning
R4


Content Structure
      1.            How should and intelligent agent solve problems.?
                        1.1.            Introduction
                        1.2.            Problem solving and search
                        1.3.            Informed search
                        1.4.            Uninformed search
                        1.5.            Local search

      2.            Game playing
                    2.1.                Min-max algorithm
                    2.2.                Alpha-beta pruning

      3.            Constraint satisfaction problems
                  3.1.                  Definition
                  3.2.                  Inference
                  3.3.                  Backtracking

      4.            How should an intelligent agent represent the world?
                        4.1.            Logic agents and Propositional Logic
                        4.2.            Inference


      5.            Is Logic sufficient for representing the world?
                        5.1.            Joint Probability Distribution
                        5.2.            Tractable distributions
                        5.3.            Probabilistic graphical models
                                          5.3.1.            Bayes Nets


      6.            Can Intelligent agent be programmed completely?
                        6.1.            Learning from data
                                          6.1.1.            Supervised Learning
                                                            6.1.1.1.            Regression
                                                            6.1.1.2.            Classification
                                          6.1.2.            Unsupervised Learning
                                                            6.1.2.1.            Clustering
                                          6.1.3.            Decision Trees

Learning Outcomes:
No
Learning Outcomes
LO1
Basic concepts of classical AI
LO2
Problem Solving using search
LO3
Knowledge representation
LO4
Learning
LO5





Part B: Learning Plan

Academic Term
First Semester 2017-2018
Course Title
Artificial Intelligence
Course No

IS ZC444
Lead Instructor
Ramprasad Joshi

Contact Hour 1
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH
CH 1
Introduction
AIMA Ch. 1
Post CH




Contact Hour 2
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH
CH 1
Introduction
AIMA Ch. 2
Post CH




Contact Hour 3
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Uninformed search
AIMA Ch.3
Post CH




Contact Hour 4
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Uninformed search
AIMA Ch.3
Post CH




Contact Hour 5
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Informed search
AIMA Ch.3
Post CH




Contact Hour 6
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Informed search
AIMA Ch.3
Post CH






Contact Hour 7
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Local search -
Hill Climbing
Simulated Annealing
AIMA CH. 4
Post CH




Contact Hour 8
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Local beam Search

Genetic algorithms
AIMA CH. 4
Post CH




Contact Hour 9
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Genetic algorithms
Tutorial
AIMA Ch. 4
Post CH




Contact Hour 10
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Game playing -
Min-Max
 Alpha-beta pruning
AIMA CH. 5
Post CH




Contact Hour 11
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Alpha- Beta pruning
AIMA CH. 5
Post CH




Contact Hour 12
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Tutorial
AIMA CH. 5
Post CH




Contact Hour 13
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Constraint satisfaction problems
-Definition
-Inference
AIMA CH. 7
Post CH




Contact Hour 14
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Constraint satisfaction problems
-Definition
-Inference
AIMA CH. 7
Post CH






Contact Hour 15
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Backtracking
AIMA CH. 7
Post CH




Contact Hour 16
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Tutorial
AIMA CH. 7
Post CH




Contact Hour 17
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH


Logic agents and proposition logic
AIMA CH. 6
Post CH




Contact Hour 18
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH


Logic agents and proposition logic
AIMA CH. 6
Post CH




Contact Hour 19
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Inference

AIMA CH. 6
Post CH






Contact Hour 20
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH


Tutorial
AIMA CH. 6
Post CH




Contact Hour 21
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Basics of probability
Slides
Post CH





Contact Hour 22
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Tutorial


Post CH




Contact Hour 23
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Tractable distributions
Slides
Post CH




Contact Hour 24
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Tractable distributions
Bayes nets
Slides
Post CH






Contact Hour 25
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Bayes Nets Inference
Slides
Post CH




Contact Hour 26
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Tutorial

Post CH




Contact Hour 27
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Supervised learning –
 Regression
R3 (Video Lectures)
Post CH




Contact Hour 28
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Supervised learning –
 Classification
R3 (Video Lectures)
Post CH




Contact Hour 29
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Unsupervised Learning-
Clustering
R3 (Video Lectures)
Post CH






Contact Hour 30
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Tutorial

Post CH




Contact Hour 31
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Decision trees
AIMA Ch. 18
Post CH




Contact Hour 32
Type
Content Ref.
Topic Title
Study/HW Resource Reference
Pre CH



During CH

Decision trees Tutorial

Post CH




 Evaluation Scheme:  
Legend: EC = Evaluation Component; AN = After Noon Session; FN = Fore Noon Session
No
Name
Type
Duration
Weight
Day, Date, Session, Time
EC-1
Quiz-I/ Assignment-I
Online
-
5%
August 26 to September 4, 2017

Quiz-II
Online

5%
September 26 to October 4, 2017

Quiz-III/ Assignment-II
Online

10%
October 20 to 30, 2017
EC-2
Mid-Semester Test
Closed Book
2 hours
30%
24/09/2017 (FN) 10 AM – 12 Noon
EC-3
Comprehensive Exam
Open Book
3 hours
50%
05/11/2017 (FN) 9 AM – 12 Noon

Syllabus for Mid-Semester Test (Closed Book): Topics in Session Nos.  1 to 16
Syllabus for Comprehensive Exam (Open Book): All topics (Session Nos. 1 to 32)
Important links and information:
Elearn portal: https://elearn.bits-pilani.ac.in
Students are expected to visit the Elearn portal on a regular basis and stay up to date with the latest announcements and deadlines.
Contact sessions: Students should attend the online lectures as per the schedule provided on the Elearn portal.
Evaluation Guidelines:
1.      EC-1 consists of either two Assignments or three Quizzes. Students will attempt them through the course pages on the Elearn portal. Announcements will be made on the portal, in a timely manner.
2.      For Closed Book tests: No books or reference material of any kind will be permitted.
3.      For Open Book exams: Use of books and any printed / written reference material (filed or bound) is permitted. However, loose sheets of paper will not be allowed. Use of calculators is permitted in all exams. Laptops/Mobiles of any kind are not allowed. Exchange of any material is not allowed.
4.      If a student is unable to appear for the Regular Test/Exam due to genuine exigencies, the student should follow the procedure to apply for the Make-Up Test/Exam which will be made available on the Elearn portal. The Make-Up Test/Exam will be conducted only at selected exam centres on the dates to be announced later.
It shall be the responsibility of the individual student to be regular in maintaining the self study schedule as given in the course handout, attend the online lectures, and take all the prescribed evaluation components such as Assignment/Quiz, Mid-Semester Test and Comprehensive Exam according to the evaluation scheme provided in the handout.



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