Subject Objectives
A student completing 6.034 will be able to: |
| |
Objective |
| 1) |
Explain the basic knowledge representation, problem solving, and learning methods of Artificial Intelligence |
| 2) |
Assess the applicability, strengths, and weaknesses of the basic knowledge representation, problem solving, and learning methods in solving particular particular engineering problems
|
| 3) |
Develop intelligent systems by assembling solutions to concrete computational problems |
| 4) | Understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering |
| And many 6.034 students will, as measured by exit survey: |
| 5) | Develop an interest in the field sufficient to take more advanced subjects |
Desired Subject Outcomes
A
student completing 6.034 will be able to: |
| |
Outcome |
How
Measured |
Related
Objective |
| 1) |
Predict
the behavior of backward-chaining rule-based systems |
H,
Q |
1,
2 |
| 2) |
Predict
the behavior and estimate the cost in time and space of various heuristic
and optimal search methods (depth-first, breadth-first, best-first, uniform-cost,
and A*), and choose the appropriate method for particular problems |
H,
Q |
1,
2 |
| 3) |
Predict
the behavior of various constraint-satisfaction methods (backtracking, forward-checking,
constraint propagation), and choose the appropriate method for particular
problems |
H,
Q |
1,
2 |
| 4) |
Develop
small logic-based, rule-based and search-based systems, predict performance characteristics,
and describe the role of rule-chaining and search in intelligent-system
engineering |
H, |
3,
4 |
| 5) |
Use
rules and logic to represent behavioral, classification, and causal knowledge
|
H,
Q |
1,
2 |
| 6) |
Apply
basic machine learning methods such as nearest neighbors, identification
trees, and neural nets |
H,
Q |
1,
2 |
| 7) |
Predict
the behavior of the basic machine-learning methods, and choose the appropriate
method for particular problems |
H,
Q |
1,
2 |
| 8) |
Modify
and extend simple implementations of the subject's representations and methods
|
H,Q |
3,
4 |
| 9) |
Develop
small learning systems, predict performance characteristics, and describe
the role of learning in intelligent-system engineering |
H |
3,
4 |
| 10) |
Discuss key issues in knowledge representation, problem solving, and learning |
H |
1, 2, 3, 4, 5, 6 |
| 11) |
Understand the relations between the techniques of KR, PS and L and traditional applications of AI to natural language processing, vision, robotics, medicine and cognitive science |
|
|
| 12) |
Use probabilistic representations and inference methods to capture uncertainty in the world and in an agent's understanding of the world |
|
|
| Key: |
| H |
Homework
|
| Q | Midterm, Final |
|
|
