Course Outline COMP6065 Artificial Intelligence (4)

Study Program Specific Outcomes

FM - BINUS - AA- FPA - 27/R2

Course Outline

COMP6065

Artificial Intelligence

(4) Study Program

Computer Science

Effective Date 01 September 2018 Revision 2

1. Course Description

This course provides students with the foundation of Artificial Intelligence, understanding of representations and external

constraints with the idea of improving students to think creatively. By completing this course, students can explain many

kinds of Artificial Intelligence algorithms, and implement those algorithms to make an application. This course is

prerequisite for Expert System, Computer Vision and Artificial Neural Network course

2. Graduate Competency

Each course in the study program contributes to the graduate competencies that are divided into employability and

entrepreneurial skills and study program specific outcomes, in which students need to have demonstrated by the time

they complete their course.

BINUS University employability and entrepreneurial skills consist of planning and organizing, problem solving and

decision making, self management, team work, communication, and initiative and enterprise.

2.1. Employability and Entrepreneurial Skills

Aspect Key Behaviour

2.2. Study Program Specific Outcomes

3. Topics

• Introduction to Artificial Intelligence

• Search Strategies

• Local Search

• Adversarial Search

• Logical Agents

• First-Order Logic

• Fuzzy Systems

• Quantifying Uncertainty I

• Quantifying Uncertainty II • Probabilistic Reasoning • Probabilistic Reasoning over Time

• Project Proposal Presentation • Learning from Examples

• Linear Regression and Classification

• Introduction to Machine Learning

• K-Nearest Neighbor • Artificial Neural Network • Support Vector Machine

• Learning Probabilistic Models

• Natural Language Processing

• Natural Language for Communication

• Introduction to Computer Vision • Project Presentation

• AI Application


COMP6065 - Artificial Intelligence | 2 Course Outline

Study Program Computer Science - Bina Nusantara University

• Introduction to Computer

4. Learning Outcomes

On successful completion of this course, student will be able to:

• LO 1: Describe what is AI and identify concept of intelligent agent

• LO 2: Explain various intelligent search algorithms to solve the problems

• LO 3: Explain how to use knowledge representation in reasoning purpose

• LO 4: Apply various techniques to an agent when acting under certainty

• LO 5: Apply various learning algorithms to solve the problems

• LO 6: Apply AI algorithms on various applications such as Game AI, Natural Language Processing, and

Computer Vision

5. Teaching And Learning Strategies

In this course, the lecturers might deploy several teaching learning strategis, including Lecture, Class discussion,

Individual Exercises, Case Studies, Project Work.

6. Textbooks and Other Resources

6.1 Textbooks

1. Andries P. Engelbrecht. (2007). Computational Intelligence: An Introduction. 2. Wiley. England. ISBN:

9780470035610 .

2. David Forsyth and Jean Ponce. (2012). Computer Vision: A Modern Approach. 2. Prentice Hall. New

Jersey. ISBN: 9780136085928 .

3. Elaine Rich, Kevin Knight, Shivashankar B. Nair. . (2010). Artificial intelligence. 3. McGraw Hill. New

York. ISBN: 0070678162 .

4. Richard Szeliski. (2011). Computer Vision: Algorithms and Applications. 1. Springer. London. ISBN:

978- 1-84882-934 .

5. Stuart Russell . (2010). Artificial intelligence : a modern approach . 03. Pearson Education . New Jersey

. ISBN: 9780132071482 .

The book in the first list is a must to have for each student.

6.2 Other Resources

1. Adversarial Search

2. AI Application

3. Artificial intelligence : a modern approach

4. Artificial Neural Network

5. First-Order Logic

6. Fuzzy Systems

7. http://ai.cs.washington.edu/www/media/papers/tmpbG3CM3.pdf

8. http://ai.stanford.edu/~koller/Papers/Getoor+al:ICML01.pdf

9. http://aitopics.net/NaturalLanguage

10. http://aitopics.net/Uncertainty

11. http://artint.info/html/ArtInt_52.html

12. http://clear.colorado.edu/~bethard/teaching/csci3202_2008/slides/bayesian-networks.pdf

13. http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture04.pdf

14. http://eprints.ucl.ac.uk/16378/1/16378.pdf

15. http://faculty.ucmerced.edu/mhyang/course/cse185/index.htm

16. http://fuzzy.cs.ovgu.de/ci/fs/fs_part01_introduction.pdf

17. http://ijcsit.com/docs/Volume%207/vol7issue5/ijcsit20160705045.pdf

18. http://intelligence.worldofcomputing.net/ai-search/uniform-cost-search.html

19. http://logic.stanford.edu/classes/cs157/2009/notes/chap02.html

20. http://theory.stanford.edu/~amitp/GameProgramming/AStarComparison.html

http://ai.cs.washington.edu/www/media/papers/tmpbG3CM3.pdf

http://aitopics.net/NaturalLanguage

http://aitopics.net/Uncertainty

http://artint.info/html/ArtInt_52.html

http://clear.colorado.edu/~bethard/teaching/csci3202_2008/slides/bayesian-networks.pdf

http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture04.pdf

http://eprints.ucl.ac.uk/16378/1/16378.pdf

http://faculty.ucmerced.edu/mhyang/course/cse185/index.htm

http://fuzzy.cs.ovgu.de/ci/fs/fs_part01_introduction.pdf

http://ijcsit.com/docs/Volume%207/vol7issue5/ijcsit20160705045.pdf

http://intelligence.worldofcomputing.net/ai-search/uniform-cost-search.html

http://logic.stanford.edu/classes/cs157/2009/notes/chap02.html

http://theory.stanford.edu/~amitp/GameProgramming/AStarComparison.html




21. http://www-users.cselabs.umn.edu/classes/Spring-2012/csci5512/slides/lec8.pdf

22. http://www-users.cselabs.umn.edu/classes/Spring-2012/csci5512/slides/lec9.pdf

23. http://www.cs.mcgill.ca/~dprecup/courses/Prob/Lectures/prob-lecture01.pdf

24. http://www.cs.mtu.edu/~nilufer/classes/cs4811/2016-spring/lecture-slides/cs4811-ch04-local-search.pdf

25. http://www.cs.ucf.edu/~mtappen/cap5415/lecs/lec6.pdf

26. http://www.cs.utah.edu/~hal/courses/2009S_AI/Walkthrough/AlphaBeta/

27. http://www.cs.utah.edu/~hal/courses/2009S_AI/Walkthrough/GreedyBFS/

28. http://www.cs.utexas.edu/~mooney/cs343/slide-handouts/nlp.pdf

29. http://www.cse.buffalo.edu/~rapaport/definitions.of.ai.html

30. http://www.cse.chalmers.se/edu/year/2013/course/TIN171/slides/chapter05.pdf

31. http://www.cse.iitb.ac.in/~nlp-ai/

32. http://www.doc.ic.ac.uk/~sgc/teaching/pre2012/v231/lecture8.html

33. http://www.mathworks.com/discovery/simulated-annealing.html

34. http://www.myreaders.info/06_Learning_Systems.pdf

35. http://www.sdsc.edu/~tbailey/teaching/cse151/lectures/chap07b.html

36. http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm

37. https://binus.ac.id/bits/learning-object/Informed-Search-A-595/#/

38. https://courses.edx.org/asset-v1:ColumbiaX+CSMM.

39. https://web.cs.hacettepe.edu.tr/~aykut/classes/fall2017/bbm406/

40. https://web.stanford.edu/class/cs276/handouts/lecture14-SVMs.ppt

41. https://www.cc.gatech.edu/~hays/compvision/

42. https://www.cc.gatech.edu/~hays/compvision/lectures/17.pdf

43. https://www.codeproject.com/Articles/203828/AI-Simple-Implementation-of-Uninformed-Search-Stra

44. https://www.techemergence.com/everyday-examples-of-ai/

45. Introduction to Artificial Intelligence

46. Introduction to Computer Vision

47. Introduction to Machine Learning

48. K-Nearest Neighbor

49. Learning from Examples

50. Learning Probabilistic Models

51. Linear Regression and Classification

52. Local Search

53. Logical Agents

54. Natural Language for Communication

55. Natural Language Processing

56. Probabilistic Reasoning

57. Probabilistic Reasoning over Time

58. Project Presentation

59. Project Proposal Presentation

60. Quantifying Uncertainty I

61. Quantifying Uncertainty II

62. Search Strategies

63. Support Vector Machine

http://www-users.cselabs.umn.edu/classes/Spring-2012/csci5512/slides/lec8.pdf

http://www-users.cselabs.umn.edu/classes/Spring-2012/csci5512/slides/lec9.pdf

http://www.cs.mcgill.ca/~dprecup/courses/Prob/Lectures/prob-lecture01.pdf

http://www.cs.mtu.edu/~nilufer/classes/cs4811/2016-spring/lecture-slides/cs4811-ch04-local-search.pdf

http://www.cs.ucf.edu/~mtappen/cap5415/lecs/lec6.pdf

http://www.cs.utah.edu/~hal/courses/2009S_AI/Walkthrough/AlphaBeta/

http://www.cs.utah.edu/~hal/courses/2009S_AI/Walkthrough/GreedyBFS/

http://www.cs.utexas.edu/~mooney/cs343/slide-handouts/nlp.pdf

http://www.cse.buffalo.edu/~rapaport/definitions.of.ai.html

http://www.cse.chalmers.se/edu/year/2013/course/TIN171/slides/chapter05.pdf

http://www.cse.iitb.ac.in/~nlp-ai/

http://www.doc.ic.ac.uk/~sgc/teaching/pre2012/v231/lecture8.html

http://www.mathworks.com/discovery/simulated-annealing.html

http://www.myreaders.info/06_Learning_Systems.pdf

http://www.sdsc.edu/~tbailey/teaching/cse151/lectures/chap07b.html

http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm

http://www.cc.gatech.edu/~hays/compvision/

http://www.cc.gatech.edu/~hays/compvision/lectures/17.pdf

http://www.codeproject.com/Articles/203828/AI-Simple-Implementation-of-Uninformed-Search-Stra

http://www.techemergence.com/everyday-examples-of-ai/




7. Schedule

Lecture

Session/Mode Related LO Topics References

1

F2F

LO 1 Introduction to Artificial Intelligence

Artificial Intelligence (AI)

Concept of Rationality

Foundations of AI

Intelligent Agent

Properties of Environment

Structure of Intelligent Agents

Introduction to Artificial Intelligence

Artificial intelligence : a modern approach

Some Definitions of "Artificial Intelligence" http://www.cse.buffalo. edu/~rapaport/definitions. of.ai.html

2

F2F

LO 1 LO 2

Search Strategies

Exercise

Informed Search Strategies

Problems Example

Searching Algorithms for Solutions

Uninformed Search Strategies

Search Strategies


Simple Implementation of

Uninformed Search

Strategies

https://www.codeproject.co

m/Articles/203828/AI-

Simple-Implementation- of-

Uninformed-Search- Stra

UCS http://intelligence.

worldofcomputing.net/ai-

search/uniform-cost-

search.html

Uninformed Search

Strategies

http://artint.info/html/ArtInt_

52.html

Greedy Best-First Search

Walkthrough

http://www.cs.utah.edu/~hal

/courses/2009S_

AI/Walkthrough/GreedyB

FS/

Introduction to A*

http://theory.stanford.edu/~

amitp/GameProgramming/A

StarComparison.html

Informed Search: A*

Algorithm

https://binus.ac.id/bits/learni

ng-object/Informed-Search-

A-595/#/

Informed Search: A* Algorithm https://binus.ac.id/bits/learning-object/Informed-earch-A-595/#/

http://www.cse.buffalo/

http://intelligence/

http://artint/

http://www.cs.utah/

http://theory.stanford/




3

F2F

LO 1

LO 2

Local Search

CSP Definition

Genetic Algorithms

Hill Climbing Search (Steepest-Ascent)

Local Search Algorithms and Optimization Problems

Local Search for CSPs

Local Search in Continuous Spaces

Local Search


Local Search

https://courses.edx.org/asse

t-v1: ColumbiaX+CSMM.

101x+2T2017_2+type@asset+block@AI_edx_SearchAgents_Local.pdf

Local Search http://www.cs.mtu. edu/~nilufer/classes/cs48 11/2016-spring/lecture-slides/cs4811-ch04-local- search.pdf

Simulated Annealing http://www.mathworks. com/discovery/simulated-annealing.html

4

F2F

LO 1

LO 2

Adversarial Search

Alpha-Beta Pruning

Exercise

Games

Imperfect Real-Time Decisions

Optimal Decision in Games (Minimax Algorithm)

Adversarial Search

Artificial intelligence

: a modern approach

Alpha-Beta Pruning http://www.cs.utah. edu/~hal/courses/2009S_AI/Walkthrough/AlphaBet a/

Minimax Algorithm http://www.cse.chalmers.se/edu/year/2013/course/TIN171/slides/chapter05.pdf

5

F2F

LO 2

LO 3 Logical Agents

Knowledge-Based Agents

Logic

Propositional Logic

The Wumpus World

Logical Agents


Propositional Logic http://logic.stanford.edu/classes/cs157/2009/ notes/chap02.html

http://www.cs.mtu/

http://www.cs.utah/

http://www.cse.chalmers/

http://logic.stanford/




6

F2F

LO 3 First-Order Logic

Exercise

First-Order Logic

Quantifiers

Resolution

First-Order Logic


: a modern approach

The Resolution Method http://www.doc.ic.ac.uk/~sgc/teaching/pre2012/v231/lecture8.html

First-Order Logic http://www.sdsc.edu/~tbailey/teaching/cse151/lectures/chap07b.html

7

GSLC

LO 3 Fuzzy Systems

Defuzzification

Fuzzification

Fuzzy Systems

Fuzzy Systems http://fuzzy.cs.ovgu.de/ci/fs/fs_part01_introdu ction.pdf

Fuzzy Logic

Fuzzy Rules

Fuzzy Sets

Inferencing

Introduction

Linguistic Variable

Membership Function

8

GSLC

LO 4 Quantifying Uncertainty I

Acting under Uncertainty

Basic Probability Notation

Exercise

Quantifying Uncertainty I


Reasoning Under Certainty http://aitopics.net/Uncertainty

9

F2F

LO 4 Quantifying Uncertainty II

Bayes Theorem

Exercise

Independence

Inference using Full-Joint Distribution

Quantifying Uncertainty II


Handling Uncertainty in Artificial Intelligence, and the Bayesian Controversy http://eprints.ucl.ac.uk/16378/1/16378.pdf

http://www.doc.ic.ac/

http://fuzzy.cs.ovgu/

http://aitopics/

http://eprints.ucl.ac/




10

F2F

LO 4 Probabilistic Reasoning

Efficient Representation of Conditional Distribution

Exact Inference in Bayesian Networks

Exercise

Representing Knowledge in an Uncertain Domain

Semantic of Bayesian Networks

Probabilistic Reasoning


Probabilistic Reasoning http://clear.colorado.edu/~bethard/teaching/csci3202_2008/slides/baye sian-networks.pdf

Probabilistic Reasoning in AI http://www.cs.mcgill.ca/~dprecup/courses/Pro b/Lectures/prob-lecture01.pdf

11

GSLC

LO 1

LO 2

LO 3

LO 4

AI Application

AI Application (Student Projects)

AI Application

Task Domains of AI

AI Application


Artificial Intelligence in

Various Domains in Life – A

Review

http://ijcsit.com/docs/Volum

e%207/vol7issue5/ijcsit201

60705045.pdf

12

GSLC

LO 1

LO 2

LO 3

LO 4

AI Application

AI Application (Student Projects)

AI Application

Task Domains of AI

AI Application


Artificial Intelligence in

Various Domains in Life – A

Review

http://ijcsit.com/docs/Volum

e%207/vol7issue5/ijcsit201

60705045.pdf

http://clear.colorado/

http://www.cs.mcgill/

http://ijcsit/

http://ijcsit/




13

F2F

LO 4 Probabilistic Reasoning over Time

Dynamic Bayesian Network

Exercise

Hidden Markov Model

Inference in Temporal Models

Markov Chain

Time and Uncertainty

Probabilistic Reasoning over Time


: a modern approach


over Time:

Part I

http://www-

users.cselabs.umn.edu/clas

ses/Spring-

2012/csci5512/slides/lec8.p

df


over Time:

Part II

http://www-

users.cselabs.umn.edu/clas

ses/Spring-

2012/csci5512/slides/lec9.p

df

14

F2F

LO 1

LO 2

LO 3

LO 4

Project Proposal Presentation

Discussion

Presentation

Project Proposal Presentation


Everyday Examples of Artificial Intelligence and Machine Learning https://www.techemergence. com/everyday-examples- of-ai/

15

F2F LO 5 Learning from Examples

Candidate Elimination Algorithm

Decision Tree

Evaluating Best Hypothesis

Exercise

Form of Learning

Supervised Learning

Theory of Learning

Version Space

Learning from Examples


Learning System http://www.myreaders.info/06_Learning_Systems.pdf

http://www-users.cselabs/




http://www/




16

F2F

LO 5 Linear Regression and Classification

Gradient Descent

Linear Classification

Linear Regression

Linear Regression and Classification


Linear Regression http://www.stat.yale. edu/Courses/1997- 98/101/linreg.htm

Linear Classification http://www.cs.ucf.edu/~mtappen/cap5415/lecs/lec6.pdf

17

GSLC

LO 5 Introduction to Machine Learning

Introduction

Machine Learning Application

What is Machine Learning?

Why is Machine Learning?

Introduction to Machine Learning


Fundamentals of Machine Learning https://web.cs.hacettepe.edu.tr/~aykut/classes/fall2017/bbm406/

18

GSLC

LO 5

LO 6

K-Nearest Neighbor

Distance Metric

K-Nearest Neighbor

Non-Parametric Model

K-Nearest Neighbor


Machine Learning https://www.cc.gatech. edu/~hays/compvision/lec tures/17.pdf

19

F2F

LO 5

LO 6

Artificial Neural Network

Backpropagation

Exercise

Introduction

Single/Multi Layer Perceptron

Artificial Neural Network


Backpropagation and Neural Networks http://cs231n.stanford. edu/slides/2018/cs231n_ 2018_lecture04.pdf

20

F2F

LO 5

LO 6

Support Vector Machine

Introduction

Linear SVM

Optimization

Support Vector Machine


Support Vector Machines

and Machine Learning on

Documents

https://web.stanford.

edu/class/cs276/handout

s/lecture14-SVMs.ppt

http://www.stat.yale/

http://www.cs.ucf/

http://www.cc.gatech/

http://cs231n.stanford/




21

F2F

LO 5

LO 6

Learning Probabilistic Models

Learning with Complete Data

Learning with Hidden Variables

Statistical Learning

Learning Probabilistic Models


: a modern approach

Learning Probabilistic Models of Relational Structure http://ai.stanford. edu/~koller/Papers/Getoo r+al:ICML01.pdf

Learning Probabilistic Behavior Models in Real-time Strategy Games http://ai.cs.washington. edu/www/media/papers/tmpbG3CM3.pdf

22

F2F

LO 6 Natural Language Processing

Information Extraction

Information Retrieval

Language Models

Text Classification

Natural Language Processing


: a modern approach

Artificial Intelligence:

Natural Language

Processing

http://www.cs.utexas.

edu/~mooney/cs343/slide-

handouts/nlp.pdf

23

F2F

LO 6 Natural Language for Communication

Augmented Grammars and Semantic Interpretation

Exercise

Machine Translation

Parsing

Phase Structure Grammars

Speech Processing

Natural Language for Communication

Artificial intelligence: a modern approach

NLP-AI

http://www.cse.iitb.ac.

in/~nlp-ai/Natural

Language:Understanding &

Generating Text & Speech

http://aitopics.net/NaturalLanguage

24

F2F

LO 6 Introduction to Computer Vision

Computer Vision Application

Computer Vision Framework

Computer Vision Theory

Introduction

Introduction to Computer Vision

Computer Vision https://www.cc.gatech. edu/~hays/compvision/

Introduction to Computer Vision

http://faculty.ucmerced.edu/mhyang/course/cse1 85/index.htm

http://ai.stanford/

http://ai.cs.washington/

http://www.cs.utexas/

http://www.cse.iitb.ac/

http://aitopics/

http://www.cc.gatech/

http://faculty.ucmerced/




25

F2F

LO 4

LO 5

LO 6

Project Presentation

Discussion

Presentation



Everyday Examples of Artificial Intelligence and Machine Learning https://www.techemergence.com/everyday-examples- of-ai/

26

F2F

LO 4

LO 5

LO 6


Discussion

Presentation



Everyday Examples of Artificial Intelligence and Machine Learning https://www.techemergence.com/everyday-examples- of-ai/

8. Evaluation

Lecture

Final Evaluation Score

Aspects Weight

Theory 100%

9. Assessment Rubric (Study Program Specific Outcomes)

LO

Indicators

Proficiency Level

Excellent (85 - 100)

Good (75 - 84)

Average (65 - 74)

Poor (<= 64)

LO 1

1.1. Ability to identify the domain

of Artificial Intelligence

At least 85% of

concept and

ideas in

domain of AI

are remarkably stated.

At least 75% of

concept and

ideas in

domain of AI

are properly

stated.

At least 65% of

concept and

ideas in

domain of AI

are fairly

stated.

Less than 65%

of concept and

ideas in

domain of AI

are stated.

1.2. Ability to identify the concept

of Intelligent Agent

At least 85% of

Intelligent

Agent concept

and ideas are

remarkably stated.

At least 75% of

Intelligent

Agent concept

and ideas are

properly

stated.

At least 65% of

Intelligent

Agent concept

and ideas are

fairly stated.

Less than 65%

of Intelligent

Agent concept

and ideas are

stated.

Assessment Activity

LO

1 2 3 4 5 6

ASSIGNMENT

FINAL EXAM

MID EXAM

http://www/

http://www/




LO 2

2.1. Ability to explain various

intelligence search algorithms

to solve the problems

Concept and

ideas of

Uninformed

Search,

Informed

Search, Local

Search, and

Adversarial

Search are clearly stated

Concept and

ideas of

Uninformed

Search,

Informed

Search, and

Local Search

are clearly

stated

Concept and

ideas of

Uninformed

Search and

Informed

Search are

clearly stated

Only concept

and ideas of

Uninformed

Search are

stated or less

2.2. Ability to express various

intelligence search algorithms

to solve the problems

Various

intelligence

search

algorithm of:

Uninformed

Search,

Informed

Search, Local

Search, and

Adversarial

Search are

properly

applied to

solve the

problems

Various

intelligence

search

algorithm of:

Uninformed

Search,

Informed

Search, and

Local Search

are properly

applied to

solve the

problems

Various

intelligence

search

algorithm of:

Uninformed

Search and

Informed

Search are

properly

applied to

solve the

problems

Only various

intelligence

search

algorithm of:

Uninformed

Search are

applied or less

LO 3

3.1. Ability to describe what

knowledge representation is

At least 85% of

knowledge

representation

concept is

remarkably stated.

At least 75% of

concept of

knowledge

representation

concept is properly stated.

At least 65% of

knowledge

representation

concept is

fairly stated.

Less than 65%

of knowledge

representation

concept is

stated.

3.2. Ability to explain how to use

knowledge representation for

reasoning purposes

Concept and

ideas of

Logical Agents,

First- Order

Logic,

Inference by

First-Order

Logic, and

Fuzzy System

are properly

presented for

reasoning purposes

Concept and

ideas of

Logical Agents,

First- Order

Logic, and

Inference by

First-Order

Logic are

properly

presented for

reasoning

purposes

Concept and

ideas of

Logical Agents

and First-Order

Logic are

properly

presented for

reasoning

purposes

Only concept

and ideas of

Logical agents

are presented

for reasoning

purposes or

less




LO 4

4.1. Ability to demonstrate various

technique to agent when

acting under uncertainty

Concept and

ideas of Basic

Probability,

Bayes

Theorem,

Bayesian

Network,

Markov Chain,

and Hidden

Markov Model

are properly demonstrated

Concept and

ideas of Basic

Probability,

Bayes

Theorem, and

Bayesian

Network are

properly

demonstrated

Concept and

ideas of Basic

Probability and

Bayes

Theorem are

properly

demonstrated

Only Concept

and ideas of

Basic

Probability are

demonstrated

or less

4.2. Ability to apply various

technique to agent when


Concept and

ideas of Basic

Probability,

Bayes

Theorem,

Bayesian

Network,

Markov Chain,

and Hidden

Markov Model

are properly

applied to

agent when


Concept and

ideas of Basic

Probability,

Bayes

Theorem, and

Bayesian

Network are

properly

applied to

agent when

acting under

uncertainty

Concept and

ideas of Basic

Probability and

Bayes

Theorem are

properly

applied to

agent when

acting under

uncertainty

Only Concept

and ideas of

Basic

Probability are

applied to

agent when

acting under

uncertainty or

less

LO 5

5.1. Ability to demonstrate various

learning algorithms to solve

the problems

Concept and

ideas of

Candidate

Elimination

Algorithm,

Linear

Regression,

Clustering, and

Classification

are properly demonstrated

Concept and

ideas of

Candidate

Elimination

Algorithm,

Linear

Regression,

and Clustering

are properly

demonstrated

Concept and

ideas of

Candidate

Elimination

Algorithm and

Linear

Regression are

properly

demonstrated

Only concept

and ideas of

Candidate

Elimination

Algorithm are

demonstrated

or less

5.2. Ability to apply various

learning algorithms to solve

the problems

Concept and

ideas of

Candidate

Elimination

Concept and

ideas of

Candidate

Elimination

Concept and

ideas of

Candidate

Elimination

Only concept

and ideas of

Candidate

Elimination

Algorithm,

Linear

Regression,

Clustering, and

Classification

are properly

applied to

solve the problems

Algorithm,

Linear

Regression,

and Clustering

are properly

applied to

solve the

problems

Algorithm and

Linear

Regression are

properly

applied to

solve the

problems

Algorithm are

applied or less

to solve the

problems