案例

C++Shooting AlgorithmParity AlgorithmAlgorithmCLink ListData StructureJavaMatlabNumerical Analysis数值分析Memory ManagementPythonScikit-imageImage ProcessingImage Filtering图像处理Financial AnalysisPortfolio ManagementVisualizationTrading StrategyStock TradingRData SamplingMonte CarloggplotSimulationGradient algorithm

For your final project, you will be writing your own code to simulate the game battleship. This project will require you to use most of the concepts we have learned in the course.

Game Battleship Simulation

Data about each grocery item will not be separated from the data for the following item; that is, the first line of input for the next grocery item will immediately follow the item which precedes it on the following line.

[Lab]Grocery Store Inventory System

In this project you will simulate an auction service in which sellers can offer items and bidders can bid on them. In particular, you will implement a class, AuctionServer, in Java that provides methods supporting all aspects of an auction.

[Project] Java Auction Server

In the numerical analysis, we would like to control and assess the errors that incurred in the math-ematical computation process, such as truncation errors and round o errors.

Matlab Numerical Analysis

A matrix is an ordered array of values arranged in rows and columns. A fundamental operation in matrix algebra is inverse

[Assignment] Matrix Operation

Write a program in scikit-image/Python to accomplish a simple image inpainting.

Python Image Filtering

Beating the market consistently is a holy grail of any money manager. In the context of stocks, the market is the stock market index.

Python Portfolio Managment

Compare dierent ways to simulate a normal random variable. You will simulate a standard normal random variable via transformations, rejection sampling, and standard R function.

R Distribution Sampling and Visualization

Simulate 2D unit square plate using a 256x256 regular grid, with initial temperature f(x, y, t = 0) = sin(2x)cos(2y).

[Homework]Temperature Simulation

Implement a steepest gradient algorithm. Use x0 = 10, y0 = 20 as starting points. You can consider that the algorithm has converged to a solution if f(x,y) is within 0.0001 of its optimal value

Unconstrained Optimization