Course Information

Introduction to Intelligent Systems - Fall 2021
Instructor: Prof. Songhwai Oh (오성회)
Email: songhwai (at) snu.ac.kr
Office Hours: Friday 2:00-4:00PM
Office: Building 133 Room 605
Course Number: 430.457
Time: MW 11:00-12:15 PM
Location: Online (Building 301 Room 302)

TA-1: Jeongwoo Oh (오정우)
Email: jeongwoo.oh (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-3: Jaeseok Heo (허재석)
Email: jaeseok.heo (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-2: Jaeyeon Jeong (정재연)
Email: jaeyeon.jeong (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-4: Minyoung Hwang (황민영)
Email: minyoung.hwang (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

  • [10/13] The midterm will be held in class on 10/20 (Wed). The exam is closed-book but you can bring one sheet (A4) of handwritten notes on a single side (the other side must be blank). You have to turn in this cheat sheet with your exam. Previous midterms: 2019, 2020.
  • [09/15] Preliminary Project 2 (due: 10/06, 23:59 KST)
  • [09/02] Preliminary Project 1 (due: 9/15, 23:59 KST)
  • [08/24] Please read the Ethics of Learning.

Schedule

Week Reading Date Lecture Date Lecture Homework
1   9/1  

 

 
2

AIMA Ch. 13, Ch. 14.1 - 14.3

9/6 9/8  
3

AIMA Ch. 14.4 - 14.5

9/13 9/15 Homework 1 (due: 9/27)
4 AIMA Ch. 15.1 - 15.3 9/20
  • Holiday
9/22
  • Holiday
 
         

Makeup lecture

 
5 AIMA Ch. 15.3 - 15.6 9/27 9/29

Homework 2 (due: 10/6)

6 AIMA Ch. 18.1 - 18.4 10/4
  • Holiday
10/6 Homework 3 (due: 10/13)
         

Makeup lecture

 
7 AIMA Ch. 18.6 - 18.7 10/11
  • Holiday
10/13  
         

Makeup lecture

 
8   10/18 10/20

Midterm

  • Time: 11:00 - 1:00PM
  • Location: 301-302

Homework 4 (due: 11/1)

9 AIMA Ch. 18.8 - 18.9 10/25 10/27  
10 AIMA Ch. 20.1 - 20.3 11/1
  • Bayesian learning
  • Learning with complete data
11/3
  • EM Algorithm
 
11 AIMA Ch. 16 11/8
  • Utility theory
11/10
  • Decision networks
 
12 AIMA Ch. 17.1 - 17.4, Ch. 21 11/15
  • Markov decision processes
  • POMDPs
11/17
  • Reinforcement learning
 
13 AIMA Ch. 25 11/22
  • Deep reinforcement learning
11/24
  • Robotics (intro)
 
14 AIMA Ch. 25 11/29
  • Localization
  • Mapping
12/1
  • SLAM
 
15 AIMA Ch. 24 12/6
  • Path planning
12/8
  • Computer vision: applications
  • Computer vision
 
         12/17  RC Car Racing Challenge  

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