It is said that nearly 90% of researchers in the United States can use MATLAB!!!
Researcher A: How do I draw a dual Y-axis graph?
Research Expert: MATLAB
Researcher A: What about a 3D dynamic graph?
Research Expert: MATLAB
Researcher A: What about principal component analysis?
Research Expert: MATLAB
Researcher A: What about pattern recognition?
Research Expert: MATLAB
Researcher A: What about image processing?
Research Expert: MATLAB
The above is a real conversation (slightly simplified).
As a researcher, you might use Office, Origin, SPSS, but you must also know MATLAB, really, because it’s not just about looking impressive; more importantly, MATLAB is a required course for research experts.
Mastering MATLAB and other mathematical software will give you wings in research.
——Ward T. Ujaln
Humanity has rapidly entered the data age, and as researchers, the data we handle is becoming increasingly complex. Basic software like Office, Origin, and Sigmaplot often seem lacking in functionality when faced with this complex data. As an essential software for American researchers, MATLAB’s capabilities extend beyond plotting to powerful statistical and data analysis, image processing, data mining, machine learning, and more, conquering researchers across various disciplines. In contrast, only about 35% of researchers in China master MATLAB, and most learn it only when they need a specific feature, often just looking it up in the MATLAB manual or Baidu, rather than studying it systematically. Such learning methods result in most people’s MATLAB skills being at the level of Researcher A (at most frequent users of certain functions), far from reaching the level of having wings.
Now, there is an opportunity to learn MATLAB. If you have a bit of a foundation in MATLAB, then this opportunity is prepared for your summer MATLAB advancement. Is this just a MATLAB training course? You are only half right. For those attending the training course, you will receive guidance from Teacher Xie, one of the founders of the MATLAB technical forum, you will clarify many concepts and skills that were previously “half-understood” to you, you will acquire many practical tips and commands that will speed up your data processing work, and you will discuss MATLAB learning experiences with peers from all over the country. Most importantly, “teaching a man to fish is better than giving him a fish”.
For this MATLAB training course, the Fengchu Technology MATLAB training team has invited the renowned Teacher Xie, who, as one of the founders of the MATLAB technical forum, has ten years of programming experience in MATLAB and has published books such as “MatLab Statistical Analysis and Usage: 40 Case Studies” and “MATLAB from Zero to Advanced”. He currently teaches in the Mathematics Department at Tianjin University of Science and Technology and has long been engaged in teaching and training related to MATLAB. He is proficient in MATLAB, SAS, R language, and other software, skilled in collaborative use of various software, with a solid theoretical foundation and rich practical experience, and detailed teaching. I believe he will lead everyone to appreciate the powerful charm of MATLAB.
Let’s get to the point:
Main Topics
|
Theme |
Course |
Training Content |
|
Morning of 28th MATLAB Programming and Data Management |
Lecture 1: MATLAB Array Operations Lecture 2: MATLAB Programming Design Lecture 3: Managing Workspace Data Lecture 4: Reading and Writing Data Files Lecture 5: Data Preprocessing |
(1) Defining and Assigning Variables, Array Operations (2) Flow Structures in MATLAB Language (3) Writing Script Files and Function Files (4) Program Debugging (5) Anonymous Functions, Subfunctions, and Nested Functions (6) On-site Practice and Q&A (7) Saving Workspace Data (8) Reading TXT File Data (9) Reading and Writing Excel File Data (10) Reading and Writing Audio Signal Data (11) Reading and Writing Image Data (12) Reading Network Data (13) Connecting to Databases (14) Merging Data Sets (15) Smoothing Data (16) Normalizing Data Transformations (17) Standardizing Data Transformations (18) On-site Practice and Q&A |
|
Afternoon of 28th – Morning of 29th MATLAB Plotting and Scientific Computing Visualization |
Lecture 6: MATLAB Plotting Lecture 7: Interactive Editing of Graphics Lecture 8: Printing and Outputting Graphics Lecture 9: Animation Production Lecture 10: GUI Design |
(1) Handle-based Graphics Objects (2) Obtaining Graphics Object Property Names and Values (3) Setting Graphics Object Property Values (4) MATLAB 2D and 3D Plotting Functions (5) Common 2D Graphs (Scatter Plot, Line Chart, Bar Chart, Area Chart, Pie Chart, Error Bar Chart, Histogram, Logarithmic Coordinate Chart, Semi-logarithmic Coordinate Chart, Multi-axis Chart, Polar Chart, Pareto Chart, Stick Chart, Stair Chart, Rose Chart, Function Graph) (6) Drawing Subplots and Coordinating Multiple Coordinate Systems (7) Modifying and Annotating 2D Graphs (Adding Titles, Axis Labels, Text Annotations, Legends, Lines, and Arrows, Setting Axis Related Properties) (8) Drawing 3D Line Graphs (9) Drawing 3D Mesh and Surface Graphs (10) Scene Effects for 3D Graphics (Color, Dying Methods, Transparency, Hollowing, Light, Lighting Effects, Viewpoint Position) Settings (11) Interactive Editing of Graphics (Selecting, Cutting, Copying, Pasting, Translating, Scaling, and Rotating Graphics Objects) (12) Automatic Code Generation for Plotting (13) Copying Graphics to Clipboard (14) Exporting Graphics to File (15) Printing Graphics (16) Producing Various Forms of Animation (17) Using GUIDE Interface Development Tool to Create Interfaces (18) On-site Practice and Q&A |
|
Afternoon of 29th Statistics and Data Analysis |
Lecture 11: Data Preprocessing Lecture 12: Descriptive Statistics and Graphs Lecture 13: Distribution, Random Numbers, and Monte Carlo Simulation Lecture 14: Parameter Estimation and Hypothesis Testing Lecture 15: ANOVA Lecture 16: Regression Analysis Lecture 17: Cluster Analysis Lecture 18: Discriminant Analysis Lecture 19: Principal Component Analysis Lecture 20: Factor Analysis Lecture 21: Neural Networks |
(1) Smoothing, Normalizing, and Range Normalization Transformations of Data (2) Descriptive Statistics and Graphs (3) Probability Calculations and Random Numbers for Common Probability Distributions (4) Monte Carlo Simulation (5) Parameter Estimation for Common Distributions (6) Testing Parameters of Normal Population (7) Fitting and Testing Distributions (8) Kernel Density Estimation. (9) One-way (Multi-way) ANOVA (10) Non-parametric ANOVA (11) Univariate (Multivariate) Linear (Non-linear) Regression (12) Cluster Analysis (13) Discriminant Analysis (14) Principal Component Analysis (or Principal Component Analysis) (15) Factor Analysis (16) BP Networks and RBF Networks (17) Data Fitting Case Analysis Based on Neural Networks (18) On-site Practice and Q&A |
|
Morning of 30th Machine Learning and Data Mining |
Lecture 22: MATLAB Data Fitting Lecture 23: Solving Clustering Problems in MATLAB Lecture 24: Solving Pattern Recognition and Classification Problems in MATLAB Lecture 25: MATLAB Big Data Processing Techniques |
(1) Interpolation Fitting (2) Regression Analysis (3) Data Fitting Based on Artificial Neural Networks (4) System Clustering (5) K-Means Clustering (6) Fuzzy C-Means Clustering (7) Clustering Analysis Based on Artificial Neural Networks (8) Distance Discrimination (9) Bayesian Discrimination (10) Fisher Discrimination (11) Pattern Recognition Based on Artificial Neural Networks (12) Pattern Recognition Based on Support Vector Machines (13) Deep Learning (14) MATLAB Big Data Processing Techniques (15) On-site Practice and Q&A |
|
Afternoon of 30th Image Processing |
Lecture 26: Medical Image Processing Lecture 27: Remote Sensing Image Processing |
(1) Reading Image Data (2) Image Binarization (3) Edge Detection (4) Image Denoising (5) Connected Region Labeling and Feature Measurement (6) Feature Point Detection and Image Registration (7) Selecting Regions of Interest (8) Discrete Fourier Transform (9) Wavelet Analysis Applications in Image Processing (10) Remote Sensing Image Filtering and Enhancement (11) On-site Practice and Q&A |
|
Afternoon of 30th Auxiliary Tutorial |
(1) Group Discussion (2) Key Issue Analysis (3) Post-learning Communication |
Participants
Researchers with some MATLAB skills who need to further improve their data analysis capabilities and increase their research article acceptance rates.
Time and Location
July 27-30, 2017 (Registration on the afternoon of July 27, classes from July 28 to July 30)
Training Center of the Zhejiang Provincial Federation of Trade Unions (No. 52, Baochu North Road, Xihu District, Hangzhou)
Learning Fees
3300 yuan/person (registration fee, training fee, materials fee, lunch fee). Group registration (≥3 people) discount of 300 yuan, early remittance discount of 100 yuan.
Accommodation Arrangements
1. Accommodation can be arranged uniformly, costs borne by participants. Accommodation: Zhejiang Provincial Federation of Trade Unions Training School, standard room 288/night/person (including breakfast for two).
2. Alternatively, nearby business hotels can be booked independently.
Registration Method
1. Please fill out the Fengchu registration form (see the original text) and send it to: [email protected]
2. Online registration (see the original text)
Course Representatives
Teacher Wang: 18368155676, QQ: 554861225
Teacher Feng: 15906640944, QQ: 419761810
Teacher Wu: 13296741173, QQ: 1489154038
Important Notes
1. Please bring your personal computer and install MATLAB software in advance.
2. Registration deadline: July 27, 2017, 17:00).
3. This meeting can provide a general VAT invoice. If needed, please fill in the title and category information according to your unit’s finance department requirements (once issued, it cannot be reissued).
4. If you need a formal invitation letter (meeting notice) in advance, please contact the course representatives.
5. Free 5A scenic area tickets (Xixi National Wetland Park).