校際選修

115-1 選課時程

進行中

  • 初選第一階段 6/15/2026
  • 初選第二階段 6/22/2026
  • 校際選修 8/24/2026
  • 初選第三階段 8/31/2026
  • 開學後加退選 9/7/2026
  • 逾期加退選 9/21/2026
選課資源

深度學習

Deep Learning

學期
114-2
學分
3 學分
當期課號
535518
永久課號
CSIC30153
開課單位
資訊科學與工程研究所
授課教師
謝秉均、彭文孝、陳永昇
校區
光復
類別
選修
上課時間表
週四
N
12:20–13:10
深度學習
EC114
3 節連堂
5
13:20–14:10
6
14:20–15:10

* 根據陽明交大上課時間表所列

概述

(1) To understand the maths of deep learning techniques (2) To familiarize with deep learning tools, such as PyTorch, TensorFlow, etc. (3) To understand the latest developments and applications of deep learning techniques (4) To develop practical working systems Students requesting to add this course, please fill out the form.: https://forms.gle/6mQVbyLBo1B7Y1tCA

先修科目

Linear Algebra, Probability Theory, Machine Learning (suggested)

教學方式

分組方式 3人/組(Paper and Final) 1人/組(Lab) 師資人力 指導教師3人 助教8人 (1) To submit final projects as academic papers (2) To hold exhibition to showcase final projects (3) To encourage students to participate in various challenges in the fields of computer vision, gaming, data analytics, etc.

評分方式

Part I (3 credits) - Deep Learning 4 Labs (including Labs 0, 2, 5, and 6) (done individually) 80% Final exam 20% Part II (3 credits) - Deep Learning Labs 4 Labs (including Labs 1, 3, 4, and 7) 50% Paper presentation (done in groups of 3 members) 25% Final project (done in groups of 3 members) 25%

週次計畫
週次主題
第 1 週Introduction & Machine Learning Basics 1. Linear Algebra 2. Probability and Information Theory
第 2 週Deep Networks 1. Deep Feedforward Networks 2. Convolutional Networks
第 3 週Convolutional Networks
第 4 週Convolutional Networks & Transformers
第 5 週Introduction to Reinforcement Learning
第 6 週No class (清明連假)
第 7 週1. Linear Factor Models 2. Autoencoders
第 8 週Valued Based Reinforcement Learning
第 9 週Diffusion Models
第 10 週Normalizing Flows
第 11 週Policy-based Reinforcement Learning
第 12 週Model-based Reinforcement Learning
第 13 週Paper Presentation
第 14 週Paper Presentation
第 15 週Final Exam
第 16 週Final Project Demo
教科書

1. I. Goodfellow, Y. Bengio, and A. Courville,Deep Learning, 1st Ed., MIT Press, Dec.2016 2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Nov. 2020

Office Hours
聯絡方式
老師 彭文孝wpeng@cs.nctu.edu.tw 陳永昇yschen@cs.nctu.edu.tw 謝秉均pinghsieh@cs.nycu.edu.tw 助教 楊賀弼mrrrimge32.cs13@nycu.edu.tw 劉子齊jonathan.tcliu.en11@nycu.edu.tw 温柏萱alison.cs13@nycu.edu.tw