機器學習

1. The goal of this course is to help students master the basic concepts and skills of machine learning that will be helpful for their study and research. 2. Students will learn how to implement machine learning algorithms. Some homeworks are about implementing machine learning algorithms in Python. 3. Students will apply what they learn to the final project.

Probability, Linear algebra, Programming in Python (preferred, not required. TA will teach basics.)

無備註

TA: 莊傑名 Chieh Ming、林佳葳 Fiona、謝孟彤 Meng-Tung、陳立杰 Barry (Office: ED716)

Homework (programming) 30%, Midterm 30%, Final project 30%, other 10%

• Introduction

• Probability Distributions

• Linear Models for Regression and Classification

• Kernel Methods

• Graphical Models

• Mixture Models and EM

• Approximate Inference

• Sampling Methods

• Hidden Markov Models

• Neural Networks and Deep Learning

Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007

地點

ED808

時間

By appointment

聯絡方式

chiahan@nctu.edu.tw