AI Workshop I and Perceptron Challenge

Girls hanging out after the AI workshop I

WORKSHOP 

 

During the AI Workshop I today, nine students including most of our research group members have been trained to understand perceptron from concepts to code-level implementation (in C language)! Thanks to Elro seniors, Qin Ying and Tiffany, presented the key perceptron concepts to us! It is our first contact with one of the most important machine learning technique- Artificial Neural Network (ANN). The perceptron is an algorithm for supervised learning. It is a type of linear classifier which can classify linearly separable data. This basic training will form the foundation for the next-level neural network- Multi-layer Perceptron (MLP).

In case you missed the workshop, or you want to review the perceptron concept in more details, you can watch the following video tutorials:

• How the Perceptron Algorithm Works 1/2  
• How the Perceptron Algorithm Works 2/2

CHALLENGE

As you finish the perceptron training, you have a new challenge to tackle - modifying the

perceptron code to classify a new data set. The information of the data set is listed in the following:

1. No.of classes: 2 (class 0 and 1)
2. No. of training data: 2000
3. No. of testing data: 400
4. No. of features: 3 (feature vector is in 3-dimensional space)
5. Files: training.txt, and testing.txt

Since the original perceptron is only for 2-dimensional data, you need to identify and modify all the dimension-related code to make it work. After you finishing the classification, visualize your training and testing data, and the decision boundary using Grapher in Mac laptop (see example). Our deadline for this challenge is March 1, 2017 (Wednesday). You can post your results onto the blog once you finish the project. Please feel free to drop by my room (501) for any questions. If you need computer resource to work on it during the break, please contact me in advance.