Introduction to Intelligent Systems (430.457) Fall 2026

Instructor: Prof. Songhwai Oh (오성회)
Email: songhwai (at) snu.ac.kr
Office Hours: Friday 3:00-4:00PM
Office: Building 133 Room 503

Course Number: 430.457
Time: MW 11:00-12:15 PM
Location: Building 301 Room 104

TA-1: Jooyoung Kim (김주영)
Email: jooyoung.kim (at) rllab.snu.ac.kr
Office: Building 133 Room 610

TA-2: Hyeondal Son (손현달)
Email: hyeondal.son (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-3: Seoyoung Lim (임서영)
Email: seoyoung.lim (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-4: Jaeseok Yang (양재석)
Email: jaeseok.yang (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.

Projects and Announcements

  • TBD

Schedule

Week

Reading

Date

Lecture

Date

Lecture

1

AIMA Ch. 1

9/2

  • Introduction

2

AIMA Ch. 12, 13.1-13.3

9/7

  • Traditional AI
  • Review of probability

9/9

  • Bayesian networks

3

AIMA Ch. 13.1-13.4

9/14

  • Exact inference in Bayesian networks

9/16

  • Approximate inference in Bayesian networks

4

AIMA Ch. 14.1-14.2

9/21

  • Dynamic models
  • Inference in dynamic models

9/23

  • Hidden Markov models
  • Kalman filtering

5

AIMA Ch. 14.4-14.5, 19.1-19.2

9/28

  • Kalman filtering (vector case)
  • Dynamic Bayesian networks

9/30

  • Supervised learning
  • Decision trees
  • Generalization error

6

10/5

(Holiday)

10/7

7

AIMA Ch. 19.3-19.4, 19.6, 22.1

10/12

  • Linear regression

10/14

  • Linear classification
  • Artificial neural networks

8

10/19

  • Midterm

10/21

9

AIMA Ch. 22

10/26

  • Deep learning (intro)

10/28

  • CNN - Part 1
  • CNN - Part 2

10

AIMA Ch. 15.1-15.4

11/2

  • RNN
  • Utility theory
  • Decision networks

11/4

  • Markov decision processes
  • POMDPs

11

AIMA Ch. 15.5-15.6, 16, 23

11/9

  • Reinforcement learning
  • Deep reinforcement learning

11/11

  • Deep reinforcement learning
  • Soft and Sparse MDPs

12

AIMA Ch. 19.7, 21.1-21.2

11/16

  • Soft and Sparse MDPs
  • Nonparametric models

11/18

  • Support vector machines
  • Bayesian learning
  • Learning with complete data

13

AIMA Ch. 21.3, 26

11/23

  • EM algorithm

11/25

  • Robotics (intro)

14

AIMA Ch. 26

11/30

  • Localization
  • Mapping

12/2

  • SLAM
  • Motion planning

15

AIMA Ch. 27

12/7

  • Computer vision

12/9

  • Computer vision: applications

TBD

  • RC Car Racing Challenge

Textbook

  • [Required] Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (4th Edition, Global), Prentice Hall, 2020/2021. (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
  • Robotic software architectures, Application domains

  • Computer Vision:

  • Image formation, Edge detection, Texture, Optical flow, Image segmentation
  • Object recognition, Reconstructing the 3D world