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1. Written Problems (a) (Bishop 5.3) We have a neural netwo...

1. Written Problems (a) (Bishop 5.3) We have a neural network y(x;w) with multiple outputs. In this problem we assume the noise on those outputs is dependent, with general covariance matrix . Each obseration tn is a vector, we can group them into a matrix T. The rst part, where is xed and k...

In this project, we will explore clustering and experiment w...

In this project, we will explore clustering and experiment with high-dimensional data. We will be using a movies dataset. In this dataset, you will find data corresponding to 4,803 movies and you are asked to implement popular algorithms like k-means and k-means++ clustering algorithms efficiently ...

2. (120 points) Programming (b) (60 points) Nearest Neighbo...

2. (120 points) Programming (b) (60 points) Nearest Neighbors For this problem, you will be implementing the k-Nearest Neighbor (k-NN) classifier and evaluating on the Credit Approval (CA) dataset. It describes credit worthiness data (in this case, binary classification)We have split the availab...

Part 1 1. Fitness is a key measure when using evolutionary...

Part 1 1. Fitness is a key measure when using evolutionary Al techniques. What does fitness refer to and why is it important? What is the fitness measure in the Travelling Salesman Problem? What was the fitness measure in Tierra? 2. Create a three-state Turing Machine rule set that works as an in...

WS-9 Bonus TASK (5 marks) Take random 10 MNIST images, each...

WS-9 Bonus TASK (5 marks) Take random 10 MNIST images, each image is 28 X 28. For each image, create an image vector of 1x784 (i.e. rows of pixels in the image are placed one after the other to form a 1D image) Put all the images together in one big image-matrix, given as: ImageVecl MNIST_Imag...

Question I Use the Boston data set available in sklearn pac...

Question I Use the Boston data set available in sklearn package. Design a linear regression model using Keras Framework. Obtain the coefficients for the linear regression model and compare it with the coefficients obtained using the sklearn.linear_model.LinearRegression. Use the following code for ...

Workshop Task 1 deep neural network 1. Recall Lecture 6. R...

Workshop Task 1 deep neural network 1. Recall Lecture 6. Run the following 3 networks, observe their behaviours, and compare in terms of (i) architecture, (ii) accuracy (both on the training and testing datasets), (iii) training time, and (iv) testing time. • net = network2.Network([784, 3...

Programming Assignment - Game Playing Algorithms Task 1 (50...

Programming Assignment - Game Playing Algorithms Task 1 (50 points): The task in this programming assignment is to implement an agent that plays the MaxConnect4 game using search. Figure 1 shows the first few moves of a game. The game is played on a 6x7 grid, with six rows and seven columns. Ther...

HW3 ● Train a discriminator/generator pair on CIFAR10 dat...

HW3 ● Train a discriminator/generator pair on CIFAR10 dataset utilizing techniques from DCGAN and Wasserstein GANs Baseline Model for DCGAN ● Generator ○ noise_input = (100,); ○ text_input = (119,); ○ # num of (hair, eyes) pairs ○ text_emb = Dense(256,‘relu’)(text_in...

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