COMP3006 (v.1) Artificial and Machine Intelligence
| Area: | Department of Computing |
|---|---|
| Credits: | 25.0 |
| Contact Hours: | 3.0 |
| TUITION PATTERNS: | The tuition pattern provides details of the types of classes and their duration. This is to be used as a guide only. Precise information is included in the unit outline. |
| Lecture: | 1 x 2 Hours Weekly |
| Tutorial: | 1 x 1 Hours Weekly |
| Equivalent(s): |
4517 (v.8)
Artificial and Machine Intelligence 300
or any previous version
|
| Prerequisite(s): |
10163 (v.10)
Unix and C Programming 120
or any previous version
OR 313670 (v.1) Engineering Programming 210 or any previous version OR Admission into 132010 (v.3) Bachelor of Engineering (Computer Systems Engineering), Bachelor of Science (Computer Science) or any previous version OR COMP1000 (v.1) Unix and C Programming or any previous version OR CMPE2004 (v.1) Advanced Engineering Programming or any previous version OR Admission into BB-CSECMP (v.1) Bachelor of Engineering (Computer Systems Engineering), Bachelor of Science (Computer Science) or any previous version AND 10926 (v.5) Mathematics 103 or any previous version OR 7062 (v.6) Mathematics 101 or any previous version OR 307535 (v.3) Engineering Mathematics 110 or any previous version OR 307536 (v.4) Engineering Mathematics 120 or any previous version OR MATH1004 (v.1) Mathematics 1 or any previous version OR MATH1010 (v.1) Advanced Mathematics or any previous version OR MATH1000 (v.1) Engineering Mathematics Specialist 1 or any previous version OR MATH1002 (v.1) Engineering Mathematics 1 or any previous version |
| UNIT REFERENCES, TEXTS, OUTCOMES AND ASSESSMENT DETAILS: | The most up-to-date information about unit references, texts and outcomes, will be provided in the unit outline. |
| Syllabus: | Artificial Intelligence, its main problems and approaches. Familiarity with symbolic representations, search methods, first-order logics and their applications in reasoning and learning tasks. Familiarity with numerical techniques in AI such as probabilistic networks and their applications. Search; Logic reasoning; Natural language Understanding; Machine Learning; Uncertainty. |
| Field of Education: | 020119 Artificial Intelligence |
| Result Type: | Grade/Mark |
Availability
| Year | Location | Period | Internal | Partially Online Internal | Area External | Central External | Fully Online |
|---|---|---|---|---|---|---|---|
| 2015 | Bentley Campus | Semester 1 | Y |
Area External refers to external course/units run by the School or Department or offered by research.
Central External refers to external and online course/units run through the Curtin Bentley-based Distance Education Area
Partially Online Internal refers to some (a portion of) learning provided by interacting with or downloading pre-packaged material from the Internet but with regular and ongoing participation with a face-to-face component retained. Excludes partially online internal course/units run through the Curtin Bentley-based Distance Education Area which remain Central External
Fully Online refers to the main (larger portion of) mode of learning provided via Internet interaction (including the downloading of pre-packaged material on the Internet). Excludes online course/units run through the Curtin Bentley-based Distance Education Area which remain Central External
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