Course Information

Introduction to Intelligent Systems - Fall 2017
Instructor: Prof. Songhwai Oh (오성회)
Email: songhwai (at) snu.ac.kr
Office Hours: Friday 2:00-4:00PM
Office: Building 133 Room 405
Course Number: 430.457
Time: MW 11:00-12:15 PM
Location: Building 301 Room 103

TA-1: Hwiyeon Yoo (유휘연)
Email: hwiyeon.yoo (at) cpslab.snu.ac.kr
Office: Building 301 Room 718

TA-3: Timothy Ha (하디모데)
Email: timothy.ha (at) cpslab.snu.ac.kr
Office: Building 301 Room 718

TA-2: Yunho Choi (최윤호)
Email: yunho.choi (at) cpslab.snu.ac.kr
Office: Building 301 Room 718

TA-4: Jaegu Choy (최재구)
Email: jaegu.choy (at) cpslab.snu.ac.kr
Office: Building 301 Room 718

Course Description


This course introduces the foundations of intelligent systems, such as probabilistic modeling and inference, statistical machine learning, computer vision, and robotics, to undergraduate students. Topics include Bayesian networks, hidden Markov models, Kalman filters, Markov decision processes, linear regression, linear classification, and nonparametric models. Students will also learn about how these methods are applied to practical applications such as computer vision and robotics. Lectures will be in English.

Project and Announcements


Schedule


Week Reading Date Lecture Date Lecture Assignment
1 AIMA Ch. 13 9/4 9/6
2 AIMA Ch. 14.1 - 14.3 9/11 9/13  
        9/15  
3 AIMA Ch. 14.4 - 14.5, Ch. 15.1 - 15.2 9/18 9/20

Dynamic models

Inference in dynamic models

4   9/25
  • No class
9/27
  • No class
 
5   10/2
  • Holiday
10/4
  • Holiday
 
6  AIMA Ch. 15.3 10/9
  • Holiday
10/11
  • Hidden Markov models
 

Textbook


  • [Required] Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (3rd edition), Prentice Hall, 2009. (AIMA Website)

Topics


  • Review of probability and linear algebra
  • Probabilistic Modeling and Inference:
    • Bayesian networks, Hidden Markov models, Kalman filters
    • Markov decision processes
  • Machine Learning:
    • Linear classification, Linear regression, Learning with complete data
    • Learning with hidden variables, EM algorithm
    • Nonparametric models, Support vector machines
    • Reinforcement learning
  • Robotics:
    • Localization and mapping, Motion planning, Planning uncertain movements, Moving
    • Robotic software architectures, Application domain
  • Computer Vision:
    • Image formation, Edge detection, Texture, Optical flow, Image segmentation
    • Object recognition, Reconstructing the 3D world