1. From Machine Learning to Deep Learning

2. Topics that I will Cover (subject to some minor adjustment) Week 2: Introduction to Deep Learning Week 3: Logistic Regression Week 4: Multi-Layer Perceptron and Back Propagation Week 5: Deep Learning (1) Week 6: Deep Learning (2)

3. Week 2: Introduction to Deep Learning Deep learning can be viewed as a recent break-through in computer science In this lecture, I will introduce you a wide range of applications of Deep Learning techniques

4. Week 3: Logistic Regression In order to understand Deep Learning, we need to first understand Artificial Neural Network We will start with a simple Artificial Neural Network: Logistic Regression We will talk about applying Logistic Regression to classification problems, and derive a learning algorithm that is called Iteratively Reweighted Least Squares (IRLS) for Logistic Regression

5. Week 4: Multi-Layer Perceptron and Back Propagation In this lecture, we will develop a multi-layer perceptron based on Logistic Regression and introduce the back propagation algorithm for training multi-layer perceptrons

6. Week 5: Deep Learning (1) In this lecture, we will talk about the issues of applying back-propagation algorithm to neural network with deep architectures Then will talk about unsupervised approaches for learning the weights for a deep neural networks layer by layer, which leads to different deep learning approaches

7. Week 6: Deep Learning (2) We will continue with week 5’s introduction on deep learning to further talk about the design, implementation, and applications of deep learning. I will talk about some of my own research work in this area, and suggest a couple of research directions that can work as Ph.D. dissertation topics.