Course Information

Introduction to Intelligent Systems - Fall 2019
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: Gunmin Lee (이건민)
Email: gunmin.lee (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-3: Minjae Kang (강민재)
Email: minjae.kang (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-2: Mineui Hong (홍민의)
Email: mineui.hong (at) rllab.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, neural networks, deep learning, 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.

RC Car Racing: A Navigation Challenge

temp_1575598587114.-2121448555.jpeg

Project and Announcements

Schedule

Week Reading Date Lecture Date Lecture Assignment
1 AIMA Ch. 13 9/2 9/4
        9/6
  • Makeup lecture
  • Time: 5-8 PM
  • Location: 301-104
 
2 AIMA Ch. 14.1 - 14.3 9/9 9/11  
3 AIMA Ch. 14.4 - 14.5 9/16 9/18
4 AIMA Ch. 15.1 - 15.7 9/23 9/25
5 AIMA Ch. 18.1 - 18.3 9/30 10/2
6 AIMA Ch. 18.4, 18.6 10/7 10/9
  • Holiday
 
7 AIMA Ch. 18.7 10/14 10/16
  • Deep learning
8 AIMA Ch. 18.8 - 18.9 10/21 10/23
  • Midterm
    • in class

 

9 AIMA Ch. 20.1 - 20.2 10/28 10/30
  • No class
10   11/4
  • No class
11/6
  • No class
 
11 AIMA Ch. 20.3, Ch. 16 11/11 11/13
12 AIMA Ch. 17.1 - 17.4, Ch. 21 11/18 11/20  
13 AIMA Ch. 25 11/25 11/27
14   12/2 12/4  
15 AIMA Ch. 24 12/9 12/11
  • Contest Week [12.9-12.13]
        12/20  

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
    • Deep learning
    • 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