BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
WORK INTEGRATED LEARNING PROGRAMMES
Digital
Part A: Content Design
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Course Title
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Artificial Intelligence
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Course No(s)
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IS ZC444
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Credit Units
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3
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Credit Model
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Content Authors
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Ramprasad Joshi
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Course Objectives
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No
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CO1
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To
give student a flavor of classical AI
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CO2
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Build
the foundation to designing Intelligent agents
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CO3
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Give
a gentle start on Machine learning
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Text Book(s)
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T1
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Artificial
Intelligence: A Modern Approach by Stuart Russell and Peter Norvig 3rd
Edition (AIMA)
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Reference Book(s)
& other resources
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R1
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Video
Lecturers:
https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x
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R2
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A.M.
Turing(1950) Computing Machinery and Intelligence Mind LIX (236): 433-460
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R3
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Video
Lecturers:
https://www.coursera.org/learn/machine-learning
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R4
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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:
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No
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Learning Outcomes
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LO1
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Basic
concepts of classical AI
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LO2
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Problem
Solving using search
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LO3
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Knowledge
representation
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LO4
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Learning
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LO5
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Part B: Learning Plan
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Academic Term
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First Semester 2017-2018
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Course Title
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Artificial Intelligence
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Course No
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IS ZC444
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Lead Instructor
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Ramprasad Joshi
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Contact Hour 1
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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CH
1
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Introduction
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AIMA
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Post CH
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Contact Hour 2
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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CH
1
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Introduction
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AIMA
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Post CH
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Contact Hour 3
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Uninformed
search
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AIMA
Ch.3
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Post CH
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Contact Hour 4
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Uninformed
search
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AIMA
Ch.3
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Post CH
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Contact
Hour 5
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Informed
search
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AIMA
Ch.3
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Post CH
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Contact
Hour 6
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Informed
search
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AIMA
Ch.3
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Post CH
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Contact Hour 7
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Local
search -
Hill
Climbing
Simulated
Annealing
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AIMA
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Post CH
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Contact Hour 8
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Local
beam Search
Genetic
algorithms
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AIMA
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Post CH
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Contact Hour 9
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Genetic
algorithms
Tutorial
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AIMA
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Post CH
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Contact Hour 10
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Game
playing -
Min-Max
Alpha-beta pruning
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AIMA
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Post CH
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Contact Hour 11
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Alpha-
Beta pruning
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AIMA
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Post CH
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Contact
Hour 12
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tutorial
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AIMA
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Post CH
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Contact Hour 13
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Constraint
satisfaction problems
-Definition
-Inference
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AIMA
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Post CH
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Contact
Hour 14
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Constraint
satisfaction problems
-Definition
-Inference
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AIMA
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Post CH
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Contact Hour 15
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Backtracking
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AIMA
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Post CH
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Contact Hour 16
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tutorial
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AIMA
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Post CH
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Contact Hour 17
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Logic
agents and proposition logic
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AIMA
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Post CH
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Contact Hour 18
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Logic
agents and proposition logic
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AIMA
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Post CH
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Contact Hour 19
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Inference
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AIMA
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Post CH
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Contact Hour 20
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tutorial
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AIMA
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Post CH
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Contact Hour 21
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Basics
of probability
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Slides
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Post CH
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Contact Hour 22
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tutorial
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Post CH
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Contact Hour 23
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tractable
distributions
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Slides
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Post CH
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Contact Hour 24
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tractable
distributions
Bayes
nets
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Slides
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Post CH
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Contact Hour 25
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Bayes
Nets Inference
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Slides
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Post CH
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Contact Hour 26
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tutorial
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Post CH
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Contact Hour 27
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Supervised
learning –
Regression
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R3
(Video Lectures)
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Post CH
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Contact Hour 28
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Supervised
learning –
Classification
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R3
(Video Lectures)
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Post CH
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Contact Hour 29
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Unsupervised
Learning-
Clustering
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R3
(Video Lectures)
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Post CH
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Contact Hour 30
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Tutorial
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Post CH
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Contact Hour 31
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Decision
trees
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AIMA
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Post CH
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Contact Hour 32
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Type
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Content Ref.
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Topic Title
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Study/HW Resource Reference
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Pre CH
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During CH
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Decision
trees Tutorial
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Post CH
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Evaluation Scheme:
Legend: EC = Evaluation Component; AN =
After Noon Session; FN = Fore Noon Session
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No
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Name
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Type
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Duration
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Weight
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Day, Date, Session, Time
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|
EC-1
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Quiz-I/ Assignment-I
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Online
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-
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5%
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August 26 to
September 4, 2017
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Quiz-II
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Online
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5%
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September 26 to
October 4, 2017
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Quiz-III/ Assignment-II
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Online
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10%
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October 20 to 30,
2017
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EC-2
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Mid-Semester Test
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Closed Book
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2 hours
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30%
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24/09/2017 (FN) 10 AM – 12 Noon
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EC-3
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Comprehensive Exam
|
Open Book
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3 hours
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50%
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05/11/2017 (FN) 9 AM – 12 Noon
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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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