This course is a graduate course that will cover both the basics and recent research
in the area of Large Scale Machine Learning and Deep Learning. The course topics are:
Machine Learning Principles
Using Scalable Data Analytics Frameworks to parallelize machine learning algorithms
Distributed Linear Regression
Distributed Logistic Regression
Linear Algebra, Probability Theory and Numerical Computation
Feedforward Deep Networks
Regularization in Deep Learning
Optimization for Training Deep Models
Convolutional Networks
Sequence Modelling: Recurrent and Recursive Nets
Generative Adverserial Networks
Deep Reinforcement Learning
Applications of Deep Learning
On successful completion of the course, the student will:
* be able to re-implement a classical machine learning algorithm as a scalable machine learning algorithm
* be able to design and train a layered neural network system
apply a trained layered neural network system to make useful predictions or classifications in an application area
* be able to elaborate the performance tradeoffs when parallelizing machine learning algorithms as well as the limitations in different network environments
* be able to identify appropriate distributed machine learning algorithms to efficiently solve classification and pattern recognition problems.
* be able to discuss, analyze, present, and critically review the very latest research advancements in the areas of Large Scale Machine Learning and Deep Learning and make connections to knowledge in related fields.
