Unsupervised

Unsupervised Feature Learning and Deep Learning

Andrew Ng

Course Description

Machine learning has seen numerous successes, but applying learning algorithms today often means spending a long time hand-engineering the input feature representation. This is true for many problems in vision, audio, NLP, robotics, and other areas. In this course, you'll learn about methods for unsupervised feature learning and deep learning, which automatically learn a good representation of the input from unlabeled data. You'll also pick up the "hands-on," practical skills and tricks-of-the-trade needed to get these algorithms to work well.

Basic knowledge of machine learning (supervised learning) is assumed, though we'll quickly review logistic regression and gradient descent.


I. INTRODUCTION



    II. LOGISTIC REGRESSION



    III. NEURAL NETWORKS



    V. APPLICATION TO CLASSIFICATION



      IV. UNSUPERVISED FEATURE LEARNING and SELF-TAUGHT LEARNING



        V. APPLICATION TO CLASSIFICATION



          VI. DEEP LEARNING WITH AUTOENCODERS



            VII. SPARSE REPRESENTATIONS



              VIII. WHITENING



                IX. INDEPENDENT COMPONENTS ANALYSIS (ICA)



                  X. SLOW FEATURE ANALYSIS (SFA)



                    XI. RESTRICTED BOLTZMANN MACHINES (RBM)



                      XII. DEEP BELIEF NETWORKS (DBN)



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