Machine Learning

1
Machine Learning

• Fall, 2008

2
Course Information

• Instructor
• Professor Suk I. Yoo (??? ??)
• Office Bld. 302, Room No. 321
• E-Mail siyoo_at_ailab.snu.ac.kr
• Text
• Machine Learning (By Tom Mitchell)
• Lecture Notes
• http//ailab.snu.ac.kr/courses.php
• Reference
• Pattern Recognition and Machine Learning
• (By Christopher Bishop)
• - http//www.machinelearning.org
• Prerequisite
• Discrete Mathematics
• Probability Theory
• Artificial Intelligence

3
Schedule

• Introduction of Machine Learning
• Concept Learning
• Decision Tree Learning
• Artificial Neural Networks Learning
• (Exam)
• Evaluating Hypotheses
• Bayesian Learning
• Instance-based Learning
• (Exam)

4
Schedule (cont.)

• Learning Sets of Rules
• Analytical Learning
• Combining Inductive and Analytical Learning
• Reinforcement Learning
• (Exam)

5
Grading Policy

• 35 equally weighted exams Class Attendance
• Up to 2 classes not attended Grade Not Changed
• 34 classes not attended 1 Step Lower Grade
• 56 classes not attended 2 Step Lower Grade
• More than 6 classes not attended Grade of F

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Types of Learning

• Supervised Learning
• Given training data comprising examples of input
  vectors along with their corresponding target
  vectors, goal is either (1) to assign each input
  vector to one of a finite number of discrete
  categories (classification) or (2) to assign each
  input vector to one or more continuous variables
  (regression).
• Unsupervised Learning
• Given training data consists of a set of input
  vector without any corresponding target values,
  goal is to either (1) to discover groups of
  similar examples within data, called clustering,
  or (2) to determine the distribution of data
  within the input space, known as density
  estimation, or (3) to project the data from a
  high-dimensional space down to two or three
  dimensions for the purpose of visualization.
• Reinforcement Learning
• Given an agent having a set of sensors to observe
  the state of its environment and a set of actions
  it can performs to alter this state, goal is to
  find suitable actions to take in a given
  situation in order to maximize the accumulated
  reward where each action resulting in certain
  state is given a reward (exploration and
  exploitation).