Course Information
Introduction to Intelligent Systems - Fall 2014
Instructor: Prof. Songhwai Oh (오성회) Email: songhwai (at) snu.ac.kr Office Hours: Friday 2:00-3:00PM Office: Building 133 Room 405 |
Course Number: 430.457 Time: TTh 5:00-6:15 PM Location: Building 301 Room 201 |
TA: Kyunghoon Cho (조경훈) Email: kyunghoon.cho (at) cpslab.snu.ac.kr Office: Building 133 Room 610 |
TA: Hyemin Ahn (안혜민) Email: hyemin.ahn (at) cpslab.snu.ac.kr Office: Building 133 Room 610 |
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, 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.
Project
- Final Project (contest: from 12/5-12/12; second due: 12/4; first due: 11/27)
- Assignment 2 (due: 11/20)
- Assignment 1 (due: 11/13)
- Team Assignment
Announcements
- [10/02] The midterm will be held in class on 10/23 (Thur). The exam is closed-book but you can bring one sheet (A4) of hand written notes on a single side (the other side must be blank). You have to turn in this cheat sheet with your exam.
- [09/04] There was a typo in lecture 2 notes. A random variable is a function from Omega (sample space) to a real number.
- [07/29] Please read the Ethics of Learning.
Schedule
Week![]() |
Reading | Date | Lecture | Date | Lecture |
---|---|---|---|---|---|
1 | AIMA Ch. 13 | 9/2 |
|
9/4 |
|
2 | AIMA Ch. 14.1-14.3 | 9/9 |
|
9/11 |
|
3 | 9/16 |
|
9/18 |
|
|
4 | AIMA Ch. 14.4-14.6, Ch. 15.1-15.2 |
9/23 |
|
9/25 |
|
5 | AIMA Ch. 15.3-15.6 | 9/30 |
|
10/2 |
|
6 | AIMA Ch. 18.1-18.4 | 10/7 |
|
10/9 |
|
7 | AIMA Ch. 18.6-18.7 | 10/14 |
|
10/16 |
|
8 | AIMA Ch. 18.8 | 10/21 |
|
10/23 |
|
9 | AIMA Ch. 18.9, Ch. 20 |
10/28 |
|
10/30 |
|
10 | 11/4 |
|
11/6 |
|
|
11 | AIMA Ch. 24 | 11/11 |
|
11/13 |
|
12 | AIMA Ch. 16.1-16.7 Ch. 17.1-17.4 |
11/18 |
|
11/20 |
|
13 | AIMA Ch. 21 | 11/25 |
|
11/27 |
|
14 | AIMA Ch. 25 | 12/2 |
|
12/4 |
|
15 | 12/9 |
|
12/11 |
|
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
- Learning with hidden variables, EM algorithm
- Nonparametric models, Support vector machines
- Reinforcement learning
- Computer Vision:
- Image formation, Edge detection, Texture, Optical flow, Image segmentation
- Object recognition, Reconstructing the 3D world
- Robotics:
- Localization and mapping, Motion planning, Planning uncertain movements, Moving
- Robotic software architectures, Application domain