Projects
- Sudoku-Solving AI Agent
- Isolation Game-Playing Agent
- Implementing a Planning Search
- Build a Sign Language Recognizer
- Dog Breed Identifier using CNNs
- Time Series Prediction and Text Generation using RNNs
- Affectiva Mimic Me
- Facial Keypoint Detection
See links for individual project requirements.
Description: In this project, you will be writing code to implement two extensions of our sudoku solver. The first one will be to implement the technique called "naked twins". The second one will be to modify our existing code to solve a diagonal sudoku. To complete this project you will use the tools you learned about in the lesson, and build upon them.
Description: Design and implement an agent to play the game of Isolation, a deterministic, two-player game in which the players alternate turns moving between cells on a regular grid till one of them has no more moves left. Use adversarial search with heuristic evaluation functions to overcome computational challenges and make your agent a smarter player.
Description: Solve air cargo logistics problems by decomposing them into individual actions using a formal planning framework, identifying dependencies and effects of each action, and generating valid plans using search techniques. Experiment with various automatically generated heuristics, including planning graph heuristics, and provide an analysis of your results.
Description: Use Hidden Markov Models to recognize gestures in American Sign Language, from individual words to complete sentences. Train it on a dataset of videos that have been pre-processed and annotated, and test on novel sequences. Improve your recognizer by finding the best English translation of each sentence.
Description: In this project, you will learn how to build a pipeline to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.
Description: In this project you'll build RNNs that can generate sequences based on input data - with a focus on two applications: With the first we'll use real market data in order to predict future Apple stock prices using an RNN model. The second one will be trained on Sir Arthur Conan Doyle's classic novel Sherlock Holmes and will generate wacky sentences based on it that may - or may not - become the next great Sherlock Holmes novel!
Description: In this project, you will learn to track faces in a video and identify facial expressions using Affectiva. As a fun visualization, you will tag each face with an appropriate emoji next to it. You will then turn this into a game where the player needs to mimic a random emoji displayed by the computer!
Description: In this project, you’ll combine your knowledge of computer vision techniques and deep learning to build and end-to-end facial keypoint recognition system. Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition. Your completed code should be able to take in any image containing faces and identify the location of each face and their facial keypoints.