This document provides an overview of a statistical pattern recognition and machine learning class, including its goals, textbook, resources, and grading. The class aims to familiarize students with probabilistic methods and basic pattern recognition techniques. Students will be expected to attend lectures, participate in discussions, present research papers, and complete several projects. Grading will be based on participation, presentations, and project work.

1. Statistical pattern recognition and machine learning Jen-Chang Liu, Spring, 2008

2. Class goal Techniques for advanced machine perception Familiar with probabilistic methods (probability distributions, estimations, Bayesian inference, graphical model) Basic pattern recognition methods (dimension reduction, PCA, clustering…)

3. Textbook Pattern Recognition and Machine Learning , Chris Bishop Reference: Introduction to machine learning , Ethem Alpaydin

4. Resources Course websites CMU, machine learning , Carlos Guestrin CMU, Advanced Machine Perception , Alexei ( Alyosha ) Efros NCNU moodle (moodle.ncnu.edu.tw)

5. Grading Students are expected to: attend the lectures, and participate in class discussions present one or a cluster of research papers do several projects