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
FM - BINUS - AA- FPA - 27/R2
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
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 3 Course Outline
Study Program Computer Science - Bina Nusantara University
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
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 4 Course Outline
Study Program Computer Science - Bina Nusantara University
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
Artificial intelligence : a modern approach
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/#/
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 5 Course Outline
Study Program Computer Science - Bina Nusantara University
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
Artificial intelligence : a modern approach
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
Artificial intelligence : a modern approach
Propositional Logic http://logic.stanford.edu/classes/cs157/2009/ notes/chap02.html
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 6 Course Outline
Study Program Computer Science - Bina Nusantara University
6
F2F
LO 3 First-Order Logic
Exercise
First-Order Logic
Quantifiers
Resolution
First-Order Logic
Artificial intelligence
: 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
Artificial intelligence : a modern approach
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
Artificial intelligence : a modern approach
Handling Uncertainty in Artificial Intelligence, and the Bayesian Controversy http://eprints.ucl.ac.uk/16378/1/16378.pdf
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 7 Course Outline
Study Program Computer Science - Bina Nusantara University
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
Artificial intelligence : a modern approach
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 : a modern approach
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 : a modern approach
Artificial Intelligence in
Various Domains in Life – A
Review
http://ijcsit.com/docs/Volum
e%207/vol7issue5/ijcsit201
60705045.pdf
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 8 Course Outline
Study Program Computer Science - Bina Nusantara University
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
Artificial intelligence
: a modern approach
Probabilistic Reasoning
over Time:
Part I
http://www-
users.cselabs.umn.edu/clas
ses/Spring-
2012/csci5512/slides/lec8.p
df
Probabilistic Reasoning
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
Artificial intelligence : a modern approach
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
Artificial intelligence : a modern approach
Learning System http://www.myreaders.info/06_Learning_Systems.pdf
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 9 Course Outline
Study Program Computer Science - Bina Nusantara University
16
F2F
LO 5 Linear Regression and Classification
Gradient Descent
Linear Classification
Linear Regression
Linear Regression and Classification
Artificial intelligence : a modern approach
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
Artificial intelligence : a modern approach
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
Artificial intelligence : a modern approach
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
Artificial intelligence : a modern approach
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
Artificial intelligence : a modern approach
Support Vector Machines
and Machine Learning on
Documents
https://web.stanford.
edu/class/cs276/handout
s/lecture14-SVMs.ppt
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 10 Course Outline
Study Program Computer Science - Bina Nusantara University
21
F2F
LO 5
LO 6
Learning Probabilistic Models
Learning with Complete Data
Learning with Hidden Variables
Statistical Learning
Learning Probabilistic Models
Artificial intelligence
: 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
Artificial intelligence
: 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
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 11 Course Outline
Study Program Computer Science - Bina Nusantara University
25
F2F
LO 4
LO 5
LO 6
Project Presentation
Discussion
Presentation
Project Presentation
Artificial intelligence: a modern approach
Everyday Examples of Artificial Intelligence and Machine Learning https://www.techemergence.com/everyday-examples- of-ai/
26
F2F
LO 4
LO 5
LO 6
Project Presentation
Discussion
Presentation
Project Presentation
Artificial intelligence: a modern approach
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
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 12 Course Outline
Study Program Computer Science - Bina Nusantara University
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
FM - BINUS - AA- FPA - 27/R2
COMP6065 - Artificial Intelligence | 13 Course Outline
Study Program Computer Science - Bina Nusantara University
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
acting under uncertainty
Concept and
ideas of Basic
Probability,
Bayes
Theorem,
Bayesian
Network,
Markov Chain,
and Hidden
Markov Model
are properly
applied to
agent when
acting under uncertainty
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
Recommended