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)