Course Website: http://advancedmachinelearning.weebly.com/

Problem Statement: The Gene Expression Omnibus (GEO) data series GSE4115 contains data from 192 human subjects, each with 22,283 profiled genes. Each subject can have one of three disease states: cancer, no cancer, or suspected cancer. Your task is to build a classifier for cancer vs. no cancer by using HDLSS techniques (such as elastic net).

Dataset: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4115

Problem Statement: You are given a dataset with images of cats and dogs in zipped file CatDogdata.zip [0.7GB]. Each image is of size 64643 each arranged in the rows of trainX matrix in file traindata.mat . Labels for training are available in the trainY matrix in file traindata.mat. You should 3-fold cross validation to create separate training and validation datasets. Testing images are provided as the rows of matrix testX in the file testdata.mat. Testing labels are not provided. You are asked to use whatever method you wish to classify the test dataset.

Dataset: https://drive.google.com/file/d/0B_ves267SeMbOHlIVWhLSThlaVU/view

Problem Statement: Train an LSTM on Human Action by Ludwig von Mises. This book is supposedly the best defense of capitalism ever written, which might be a good read for your winter vacation. For the assignment, you don't have to read it. Just train an LSTM on it and generate five samples of random text that sound like this book. Submit a single zip archive with well-commented code, sample output, and any interesting observations while training the code.