Course Objectives
(1) Understand the methods of search and apply them to various problems.
(2) Understand the various knowledge expressions and the reasoning methods that use them.
(3) Understand neural networks and machine learning on them.
Rubric
| Ideal Level | Standard Level | Unacceptable Level |
Achievement 1 | Fully understand and apply search techniques to a variety of problems. | Generally understand search techniques and can apply them to a number of problems. | Do not understand the search technique and cannot apply it to problems. |
Achievement 2 | Fully understand and can explain the various knowledge expressions and the inference methods that use them. | Generally understand and can explain the various knowledge expressions and the reasoning methods that use them. | Do not fully understand and cannot explain the various knowledge expressions and reasoning methods that use them. |
Achievement 3 | Fully understand and can explain neural networks and machine learning on them. | Generally understanding and can explain neural networks and machine learning on them. | Do not understand enough about neural networks and machine learning on them, and cannot explain them. |
Assigned Department Objectives
Teaching Method
Outline:
Describe the basic concepts and techniques of artificial intelligence. In particular, the focus will be on various search techniques and their use to solve problems, knowledge expressions and their use, neural networks and machine learning on them.
Style:
The lecture is mainly based on the content of textbook, but should be supplemented with handouts if required. Also, tasks will be assigned as appropriate. The contact person is Yukihiro Hamada.
Notice:
It is desirable to have a thorough understanding of the content of year 4 classes Discrete Mathematics and Data Structure and Algorithms. Also, it is desirable for students to have acquired any programming language, since it is necessary to have an algorithmic understanding of various methods. This course's content will amount to 90 hours of study in total. These hours include the learning time guaranteed in classes and the standard self-study time required for pre-study / review, and completing assignment reports.
Students who miss 1/3 or more of classes will not be eligible for evaluation.
Characteristics of Class / Division in Learning
Course Plan
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|
|
Theme |
Goals |
2nd Semester |
3rd Quarter |
1st |
Artificial intelligence overview |
Can explain outline of artificial intelligence research by viewing the history of artificial intelligence research from several perspectives.
|
2nd |
Problem solving and search |
Can explain problem solving as state space search. Understand the steps of vertical and horizontal searching and apply them to problem solving.
|
3rd |
Limited branch search |
Understand the cost-aware search and can find the best solution using the limited branch search.
|
4th |
Heuristic search |
Understand and conduct search using estimated costs to the goal (heuristic search).
|
5th |
Search for And/Or graphs |
Understand that problem-solving by problem-breaking methods and game-state-space exploration by two-person becomes a search for And/Or graphs and can apply it to problem-solving.
|
6th |
Knowledge representation using predicate logic |
Understand the syntax of predicate logic and use logical expressions to express propositional knowledge.
|
7th |
Proof system based on the fusion principle |
Understand the proof system based on the principle of fusion and the secular form, which is one of the standard forms of predicate logic, and can carry out deductive and proving using it.
|
8th |
Midterm exam It is given during class. |
|
4th Quarter |
9th |
Production System |
Understand and can explain the basic operation of a production system.
|
10th |
Semantic Network and Frame |
Understand and can explain knowledge representation and simple reasoning using a semantic network. Also, understand and can explain knowledge representation using a frame.
|
11th |
Perceptron |
Understand the basic operation of neurocells and can explain the operation and learning of the perceptron.
|
12th |
Backpropagation |
Conceptually understand and can explain the learning by backwards propagation of errors in a feed-forward network.
|
13th |
Auto encoder |
Understand and can explain how auto-encoders (self-encoding units) work and the pre-learning of feed-forward networks using auto-encoders.
|
14th |
Recurrent Neural Network |
Conceptually understand and can explain the behavior of the recurrent neural network and its special case of the Hopfield Network .
|
15th |
Deep learning |
Understand outline and can explain some examples of deep learning as a combination of different network configurations and learning techniques.
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16th |
Final exam
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Evaluation Method and Weight (%)
| Examination | Task | Mutual Evaluations between students | Behavior | Portfolio | Other | Total |
Subtotal | 80 | 20 | 0 | 0 | 0 | 0 | 100 |
Basic Proficiency | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Specialized Proficiency | 80 | 20 | 0 | 0 | 0 | 0 | 100 |
Cross Area Proficiency | 0 | 0 | 0 | 0 | 0 | 0 | 0 |