== This deals with the second half of the course with the above title. The first half was taught by Prof. Naveen M. B. The second half mostly starts by looking at a glimse of what learning from data without assuming any information about the distribution generating the data; essentially searching for the pattern, if any, in the data. Let us start by looking at the following data-blue points above the hyperplane while red dots are below. Some aspects of the concept below have already been dealt with in the first half. So, it is a repetition. -----------------------image ------------------------ Figure 1: The figure shows a simple data set that can "linearly separated" ofcourse by a hyperplane. Although stylized, studying the above provides a lot of insights. First, note that the above can be generated randomly from a distribution but with the caveat that it can be separated by a plane. Now, we shall ask what do we mean by separating the data by a hyperplane? What is a hyperplane? Towards this, consider the following picture of a hyperplane.

B. N. Bharath Assistant professor, Department of Electrical, Electronics and Communication Engineering (EECE), IIT Dharwad Commenting Please write your name and roll number followed by your comments. This helps me know who has commented. Also, please do not hesitate to comment. All the comments will be taken seriously, and no comment is a stupid comment :grinning: Logistics There will be two evaluations-one quiz ($40%$) and one final exam ($60%$). The quiz may contain some assignments (given during the regular classes) as well like writing a code, solving problems etc.

by Sumit Sah Overview Example Applications Basic Motivation to Neural Networks What is DL? Why do you need DL?Some Applications Motivation through examples Problem: Find if the incoming email is SPAM or NOT A SPAMFeed in the email to a computer, and it should tell you if it is a spam or not