資料科學與 Python 程式實務

This course aims to extend student’s programming ability with several practical projects. Each project has its physical background. We will show you how to achieve the objective by programming. By the end of the semester, you are expected to have the ability to solve practical engineering issues via programming.

Basic python programming

無備註

教師未提供此項資料

作業、專題

• Eigenvalues and Eigenvectors

  1. Eigenvalues and Eigenvectors Problem statements 2. The Power Method 3. The QR Method 4. Eigenvalues and Eigenvectors in Python

  講授：

  2

  示範：

  2

  實作：

  2

• Numerical Linear Algebra

  1. Basics of Linear Algebra 2. Linear Transformations 3. Systems of Linear Equations 4. Solutions to Systems of Linear Equations 5. Solve Systems of Linear Equations in Python 6. Matrix Inversion

  講授：

  2

  示範：

  2

  實作：

  2

  其他：

  0

• Least Squares Regression

  1. Least Squares Regression Problem statements 2. Least Squares Regression Derivation (Linear Algebra) 3. Least Squares Regression Derivation (Multivariant Calculus) 4. Least Squares Regression in Python 5. Least Squares Regression for Non-linear Functions

  講授：

  2

  示範：

  2

  實作：

  2

  其他：

  0

• Advanced Python Programming with Applications

  1. Knapsack Problems and Dynamic Programming 2. Graph Optimization Problems 3. Random Walk and Data Visualization 4. Monte Carlo Method, Sampling, and Confidence Intervals 5. Randomized Trials and Hypothesis Checking

  講授：

  12

  示範：

  6

  實作：

  6

  其他：

  0

• Introduction to Machine Learning

  1. Machine Learning Basics 2. Modeling and Computation 3. Regression, Linear Models, and Analytical Solutions: Prediction for Boston Housing Price

  講授：

  2

  示範：

  2

  實作：

  2

None

地點

教師未提供此項資料

時間

教師未提供此項資料

聯絡方式

教師未提供此項資料