livst / cse512_machinelearning

Homework for Machine Learning Course @ Stony Brook University

CSE512_MachineLearning

Homework 1

Q1: Probability (Expectation, Variance, Covariance)
Q2: Paramter Estimation (MLE, MAP, Estimator Bias)
Q3: Regression and MLE
Q4: Ridge Regression and LOOCV
Q5.2: Implement coordinate descent to solve the Lasso
Q5.4: Implement predicting the number of stars of a Yelp review

Homework 2

Q1: Naive Bayes and Logisitic Regression
Q2: Support Vector Machines
Q3.1: Implement Kernel SVM using Quadratic Programming
Q3.2: Implement Linear SVM using Stochastic Gradient Descent
Q4: Implement Hard Negative Mining for object detection

Homework 3

Q1: Boosting
Q2: PCA via Successive Deflation
Q3: Implement K-means clustering algorithm
Q4: Implement Kernel SVM for scene classification

Homework 4

Q1: EM for GMM
Q2: EM for HMM with tied mixtures
Q3: Implement Linear-Chain Hidden CRF for gesture recognition