Course Code
|
科目名稱
|
人工智能之基本原理
Fundamentals of
AI
|
|||||||||
教員
|
學 分
|
||||||||||
課程性質
|
Computer
Science Steam 1必修課程
|
同科其他選擇
|
|||||||||
Workload
|
l 非PAPER類功課
l FINAL EXAM
l Lab
*ESTR 3108 比 CSCI3230 多一份AI project, 其他所有野一樣. 呢份AI project 要自學好多野, e.g. tensorflow |
好重
|
|||||||||
重
|
1
|
||||||||||
平均
|
|||||||||||
輕
|
2
|
||||||||||
極輕
|
|||||||||||
評價教學內容
|
#1 無midterm, 好容易唔主動去溫書。Lab有準備就唔難，最好識少少prolog。assignment好似唔會email通知，要定期上website
check
#2 AI有趣但範圍太廣, 唔好比題目吸引到。 #3 bore, ppt not deep enough. don't take this course unless your stream required to; U will need a GPU to finish final project [admin案：粗口已過濾] |
||||||||||
評價教員教學
|
#1 落堂可以問佢野，前提係你知自己唔明啲咩
#2 ppt bad, too many words on the ppt; 最好要自己溫先明講咩 #3 lesson bore [admin案：粗口已過濾] |
||||||||||
CUSIS科目資料
|
Description：
Basic concepts
and techniques of artificial intelligence. Knowledge representation:
predicate logic and inference, semantic networks, scripts and frames, and
object-oriented representation. Searching: such as A*, hill-climbing, minimax
and alpha-beta pruning. Planning: the frame problem and the STRIPS formalism,
representation schemes and planning strategies. Neural networks: learning
algorithms, neural architecture and applications. Natural language
processing. Knowledge acquisition and expert systems: properties, techniques
and tools of expert systems.
Learning
Outcome：
Students will
be able to:
1.Use agents to
model AI problems
2.Use search
techniques such as A* to search for optimal solutions for AI problems and to
play games.
3.Use various
logics to represent knowledge and to do reasoning and build expert systems.
4.Use computer
learning techniques to acquire real life knowledge in an appropriate
representation model (e.g. decision tree and neural networks).
5.Derive
learning rules from first principle.
6.Solve real
life problems (e.g.classifications and prediction) by such models.
7.Estimate complexity
of AI algorithms and prove theorems by contradiction and other techniques.
8.Use computer
vison techniques such edge detection to extract features.
|
||||||||||
其他資料
|
Course Outline：
https://drive.google.com/open?id=1mc71plMjbd8gQWKPs0ZKjgBz4rfoyXNY 過往註冊資料： 2017Sem1：學位 130|註冊 128|剩餘2 [CSCI] 2017Sem1：學位 32|註冊 32|剩餘0 [ESTR] 2018Sem1：學位 130|註冊 113|剩餘 17 [CSCI] 2018Sem1：學位 30|註冊 21|剩餘 9 [ESTR] | ||||||||||
同學推薦
|
高度推薦
|
推薦
|
有保留
|
2
|
極有保留
|
1
|
|||||
CSCI3230