歡迎報名參加清大計通中心舉辦MATLAB訓練課程，請選擇最適合您的內容報名參加。

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Simulink 基礎概念

本課程為 Simulink 入門，將從 Simulink 的操作環境、基本架構、函式庫功能介紹開始。接著說明如何建立 Simulink 模型以及子系統，利用子系統將模組以階層式結構呈現，並將自訂的子系統設定為可重複使用的 function library。課堂中，您還會學習如何將 Simulink 與MATLAB 的功能做結合，發揮系統模擬的最大功效。課堂最後將介紹 Simulink 的重要程式設計技巧，並教您如何利用其除錯功能減少錯誤發生。

• Introduction
• Building the Model
• Creating A Simple Model
• Masking Subsystem
• Working with Subsystems
• Working with MATLAB
• Modeling Discrete Systems
• Modeling Continuous and Hybrid Systems

如何使用MATLAB進行大數據分析

本課程介紹MATLAB如何面對大量資料的處理與分析，主要可分為三個部分，第一部分介紹資料的匯入，以及MATLAB匯入檔案時，如何有效率的使用記憶體；第二部分將介紹資料前處理的相關功能，如何將資料結構化，並合理的補齊缺漏值，最後一部分將介紹分析的模型與手法，並如何從如何從資料分析的結果中獲得價值。

• Big Data Capability in MATLAB
• Big data in industry
• New Big Data Capabilities in MATLAB
• Access Big Data
• Datastore
• Exercise1 : 'datastore'
• Tall arrays
• Working with tall arrays
• Exercise2 : tall array
• Functions support tall
• Conclusion and Other Materials

基於MATLAB的最佳化技術

本課程介紹MATLAB與最佳化相關的工具:最佳化工具箱及全域最佳化工具箱。課程包含將問題公式化、選擇合適的最佳化函式求解、以及使用互動式之使用者圖形介面(GUI)求解最佳化問題。學員可透過實用範例與上機練習了解其重要概念。

• Applied Optimization
• Specifying the Objective Function
• Choosing a Solver
• Global Optimization

MATLAB於影像處理的應用

在本課程中，你將了解用MATLAB與電腦視覺系統工具箱來實現多種電腦視覺應用，包括目標偵測，人臉特徵追蹤和立體視覺。此外，我們將展示如何在MATLAB中將CV演算法轉成C程式碼，以便進一步使用。

• Streaming Processing (System Object)
• Featured-Based Workflow
• Image Category Classification
• Object Detection and Stereo Vision

※Please note: This workshop will be conducted in Mandarin※

Join us for MATLAB Training Courses, please refer to following section for more workshop details.

《Venue》CCC – Computer Laboratory

Any inquiries,
please mail to the following address: inquiry@cc.nthu.edu.tw or call Miss Chiang at campus ext.31231

Focus on software fundamental skills, perfect for beginner and any faculty.

Registration : https://forms.gle/eBqsu8rCjsRM4pkh6

Simulink Basic

This course is an introduction to Simulink, starting with the introduction of Simulink's operating environment, basic architecture, and library functions. Then explain how to create Simulink models and subsystems, use subsystems to present modules in a hierarchical structure, and set custom subsystems as reusable function libraries. In this course, you’ll also learn how to combine the functions of Simulink and MATLAB to maximize the effectiveness of system simulation. The class concludes by introducing important programming techniques in Simulink and teaching you how to use debugging function to find the bug of your design algorithm.

• Introduction
• Building the Model
• Creating A Simple Model
• Masking Subsystem
• Working with Subsystems
• Working with MATLAB
• Modeling Discrete Systems
• Modeling Continuous and Hybrid Systems

Big Data Analytics with MATLAB

This course introduces how MATLAB handles and analyzes a large amount of data. It can be divided into three parts. The first part introduces the import of data and how to use memory efficiently when MATLAB imports files; the second part introduces the relevant functions of data pre-processing, how to structure the data, and fill in the missing values reasonably; the last part will introduce the analysis model and method, and how to get value from the results of data analysis.

• Big Data Capability in MATLAB
• Big data in industry
• New Big Data Capabilities in MATLAB
• Access Big Data
• Datastore
• Exercise1 : 'datastore'
• Tall arrays
• Working with tall arrays
• Exercise2 : tall array
• Functions support tall
• Conclusion and Other Materials

Optimization based on MATLAB

This course introduces applied optimization in the MATLAB environment, focusing on using Optimization Toolbox™ and Global Optimization Toolbox™.

• Applied Optimization
• Specifying the Objective Function
• Choosing a Solver
• Global Optimization

MATLAB in image processing

In this course, you’ll learn about using MATLAB and the Computer Vision Toolbox to implement a variety of computer vision applications, including object detection, facial feature tracking, and stereo vision. Furthermore, we will show how to convert the CV algorithm into C code in MATLAB for further use.

• Streaming Processing (System Object)
• Featured-Based Workflow
• Image Category Classification
• Object Detection and Stereo Vision