Course Information

Introduction to Intelligent Systems - Fall 2025

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

TA-3: Hosung Lee (이호성)
Email: hosung.lee (at) rllab.snu.ac.kr
Office: Building 301 Room 718

TA-2: Jooyoung Kim (김주영)
Email: jooyoung.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.

RC Car Racing Challenge

Project and Announcements

Schedule

Week
Reading
Date
Lecture
Date
Lecture
1
 AIMA Ch. 1
 9/1
  • Introduction
9/3
  • Introduction to ROS
2

AIMA Ch. 12, Ch. 13.1 - 13.3

9/8
  • Traditional AI
  • Review of probability
9/10
  • Bayesian networks
3
AIMA Ch. 13.1 - 13.4
9/15
  • Exact inference in Bayesian networks
9/17
  • Approximate inference in Bayesian networks
4

AIMA Ch. 14.1 - 14.2

9/22
  • Dynamic models
  • Inference in dynamic models
9/24
  • Hidden Markov models
  • Kalman filtering
5

AIMA Ch. 14.4-14.5, Ch. 19.1 - 19.2

9/29
  • Kalman filtering (vector case)
  • Dynamic Bayesian networks
10/1
  • Supervised learning
  • Decision trees
  • Generalization error
6
 
10/6
  • (Holiday)
10/8
  • (Holiday)
7

AIMA Ch. 19.3 - 19.4, 19.6

AIMA Ch. 22.1

10/13
  • Linear regression
10/15
  • Linear classification
  • Artificial neural networks
8
 
10/20
  • Midterm
    - Time: 11:00 AM
    - Location: in class
10/22
  • (No class)
9
AIMA Ch. 22
10/27
  • Deep learning (intro)
10/29
  • CNN - Part 1
  • CNN - Part 2
10

AIMA Ch. 15.1 - 15.4

11/3
  • RNN
  • Utility theory
  • Decision networks
11/5
  • Markov decision processes
  • POMDPs
11

AIMA Ch. 15.5 - 15.6, Ch. 16

AIMA Ch. 23

11/10
  • Reinforcement learning
  • Deep reinforcement learning
11/12
  • Deep reinforcement learning
  • Soft and Sparse MDPs
12

AIMA Ch. 19.7

AIMA Ch. 21.1 - 21.2

11/17
  • Soft and Sparse MDPs
  • Nonparametric models 
11/19
  • Support vector machines
  • Bayesian learning
  • Learning with complete data
13

AIMA Ch. 21.3

AIMA Ch. 26

11/24
  • EM algorithm
11/26
  • Robotics (intro)
14
AIMA Ch. 26
12/1
  • Localization
  • Mapping
12/3
  • SLAM
  • Motion planning
15
AIMA Ch. 27
12/8
  • Computer vision
12/10
  • Computer vision: applications
 
 
 
 
12/12
  • RC Car Racing Challenge
    - Time: 12:30 PM
    - Location: Building 300 Room 201

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