Artificial Intelligence: Cognitive Robotics (2013 Spring)

  • 2013 Spring Semester Undergraduate Course for Computer Science and Engineering
  • Instructor: Prof. Byoung-Tak Zhang
  • TA: Eun-Sol Kim (eskim ‘at’ bi.snu.ac.kr)
  • Classroom: Rm. 209, Bldg. 302 /  Rm. 311-1, Bldg, 302 (Thu)
  • Time: Tue & Thu, 09:30-10:45
  • Text:

o    [1] The Quest for Artificial Intelligence, Nilsson, N. J., Cambridge University Press, 2009.

  • References:

o    [1] Cognitive computing I: Multisensory perceptual intelligence in real-world (in Korean), B.-T. Zhang, Mu-Song Yeu, Communications of KIISE, 30(1):75-87, 2012.

o    [2] Cognitive computing II: Machine vision-language learning in real-life (in Korean), B.-T. Zhang, Dong-Hoon Lee, Communications of KIISE, 30(1):88-100, 2012.

o    [3] Cognitive computing III: Deep dynamic prediction in real-time (in Korean), B.-T. Zhang, Hyun-Soo Kim, Communications of KIISE, 30(1):101-111, 2012.

o    [4] Next-generation machine learning technologies (in Korean), B.-T. Zhang, Communications of KIISE, 25(3):96-107, 2007.

  • Evaluation:

o    Two open-book exams (60%)

o    Project (30%)

o    Reading and quiz (5%)

o    Attendance and discussion (5%)

  • Goal:

o    To understand the history of ideas and achievements in artificial intelligence (AI)

o    To get theoretical basis for developing computational models of AI

o    To acquire the conceptual and technical tools for application of artificial intelligence

o    To understand the future prospects of human-level artificial intelligence

o     

  • Notice
    • Previous Midterm Exam
    • Questions for Final Exams are uploaded
    • Deadline for sending Final Project Poster Files is 6/9 23:59
    • Templete and Example of Final Project are reuploaded
    •  
  • Dataset(Original dataset)

o    MNIST_800 – 10 classes, 800 examples

o    MNIST_2000 – 10 classes, 2000 examples

o    CIFAR – 2 classes, 3092 features

o    CIFAR4_Training, CIFAR4_Test – 4 classes, 3092 features

 

  • Dimension Reduced Dataset

o    CIFAR_HoG_2class_training – 2 classes, HoG dataset (324 features)

o    CIFAR_HoG_2class_test - 2 classes, HoG dataset (324 features)

o    CIFAR_HoG_4class_training – 4 classes, HoG dataset (324 features)

o    CIFAR_HoG_4class_test – 4 classes, HoG dataset (324 features)

o    CIFAR_HoG_10class_training – 10 classes, HoG dataset (324 features)

o    CIFAR_HoG_10class_test – 10 classes, HoG dataset (324 features)

 

 

 

 

 

 


  • Lecture Schedule

 

Week

Topics

Slides

Questions

1

3/5

Thinking machines:
Leibniz, Frege, Turing, AI pioneers

ch.1, ch.2, ch.3

Q1

3/7

Heuristic programs:
logic, chess, checkers, 8-puzzle, GPS

ch.3, ch.5, ch.14

Q2

2

3/12

Practice

Introduction

 

3/14

Neural networks and supervised learning algorithms

NN, Ref [4]

 

3

3/19

Mobile robots:
Shakey, FREDDY, tour guides, service robots

ch.10, ch.12, ch.32

Q3

3/21

  New generation projects:

FGCS, ESPRIT, SC

ch.8, ch.16, ch.21
ch.22
, ch.23

Q4

4

3/26

Practice

Perceptron

 

3/28

Practice

Weka

 

5

4/2     

Bio-inspired AI:
brain-style computing, simulating evolution

ch.4, ch.24, ch.25, ch.35, Ref [3]

Q5

4/4

Computer vision

ch.9, ch.20, ch.30

Q6

6

4/9

Natural language & speech:
Q&A, MT, NLP, HEARSAY-II

ch.7, ch.13,

ch.17 ch.19, ch.30

Q7

4/11

Practice

 

7

4/16

Review

QR1

4/18

Mid-term Exam

 

8

4/23

Mid-term Presentation

(Mid-term Report, due: 4.22)

 

4/25

Practice

9

4/30

Expert systems:
DENDRAL, MYCIN, PROSPECTOR, XCON

ch.15, ch.18

Video 1

Video 2

Q8

5/2

-          Knowledge representation & reasoning:
semantic net, frames

-          Symbolism and Connectionism

ch.6, ch.11
ch.26
,ch.27

Ref [3]

 

10

5/7

Practice

Practice4

5/9

Practice

Practice5

 

11

5/14

Machine learning:
MBL, CBR, DT, NN, kNN, RL

ch.29,

Ref [4]

 

5/16

Probabilistic graphical models (1):
LVM, BN, DBN

 ch.27, ch.28

 

12

5/21

Intelligent agents:
recommenders, assistants, IR, UI

ch.31,ch.33,ch.34

5/23

-          Cognitive robots:
EU FP 6&7, humanoids, personal robots

-          Human-leve AI:
Self-driving robot cars,     Watson, Siri

Ref [1]

Ref [2]

Ref [3]

13

5/28

Practice

 

 

5/30

Review

QR2

14

6/4

Final Exam

 

15

6/11

Final Presentation (Poster)

Poster Example

Poster Template


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