Course Information

Introduction to Intelligent Systems - Fall 2020
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: Online (Building 301 Room 103)

TA-1: Jeongho Park (박정호)
Email: jeongho.park (at) rllab.snu.ac.kr
Office: Building 301 Room 814

TA-3: Wooseok Oh (오우석)
Email: wooseok.oh (at) rllab.snu.ac.kr
Office: Building 301 Room 814

TA-2: Hogun Kee (기호건)
Email: hogun.kee (at) rllab.snu.ac.kr
Office: Building 301 Room 814

TA-4: Jae Eun Kim (김재은)
Email: jaeeun.kim (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, nonparametric models, and reinforcement learning. 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 Homework
1    9/2 9/4  
2 AIMA Ch. 13, Ch. 14.1 - 14.3 9/7 9/9  
3 AIMA Ch. 14.4 - 14.5 9/14 9/16

Homework 1 (due: 9/23)

4 AIMA Ch. 15.1 - 15.3 9/21 9/23  
5 AIMA Ch. 15.3 - 15.6 9/28 9/30
  • Holiday
 
         

Homework 2 (due: 10/7)

6 AIMA Ch. 18.1 - 18.3, 18.4 10/5 10/7 Homework 3 (due: 10/14)
7 AIMA Ch. 18.6 10/12 10/14  
8 AIMA Ch. 18.7 10/19 10/21
  • Midterm
  • Time: 11:00-12:15 PM
  • Location: 301-302
 
9   10/26 10/28

Homework 4 (due: 11/4)

10 AIMA Ch. 18.8 - 18.9 11/2
  • Nonparametric models
11/4
  • Support vector machines
Homework 5
11 AIMA Ch. 20.1 - 20.3 11/9
  • Bayesian learning
  • Learning with complete data
11/11
  • EM Algorithm
Homework 6
12 AIMA Ch. 16 11/16
  • Utility Theory
11/18
  • Decision networks
 
13 AIMA Ch. 17.1 - 17.4, Ch. 21 11/23
  • Markov decision processes
  • POMDPs
11/25
  • Reinforcement learning
 
14 AIMA Ch. 25 11/30
  • Deep reinforcement learning
12/2
  • Robotics (intro)
 
15 AIMA Ch. 25 12/7
  • Localization
  • Mapping
12/9
  • SLAM
 
16 AIMA Ch. 24 12/14
  • Path planning
12/16
  • Computer vision: applications
  • Computer vision
 
        12/18
  • RC Car Racing: A Navigation Challenge
  • Time : TBA
  • Location: TBA
 

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