Students implement depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world.
HW2 - Multi-agent Search:
Classic Pacman is modeled as both an adversarial and a stochastic search problem. Students implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions.
HW3 and HW4 is for Machine Learning Part of the course
HW3 - Linear Model, Decision Tree, and Random Forest
The goal of this programming part is to implement a supervised machine learning pipeline from scratch, which includes preprocessing, training, and evaluation of linear and nonlinear models on a given dataset.
Apply this pipeline to a classification task and a regression task.
Including preprocessing, models, training, and evaluation.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent