MATH FOR #ML

Overview:

1. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications.

2. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

3. In this event we will be inviting a data scientist who is currently working on the domain to deliver the talk.

4. With his/her expertise he/she can answer the basic underlying questions such as the amount of and level of mathematics that is required to understand an interdisciplinary field such as Machine learning.

5. This talk will cover the introduction and familiarization of topics namely, Linear Algebra, Probability Theory and Statistics, Multivariate Calculus and their use in Algorithms and Optimizations in Machine Learning.

6. Data Scientist who is currently researching on this domain will be better able to bring out actual problems faced while implementing a project and teach how to overcome them.

Outcome:

1. To understand the career opportunities in the field of Data Science.

2. Choose the right algorithm(s) for the problem.

3. Make good choices on parameter settings, Validation strategies.

4. Recognize over / under fitting.

5. Troubleshoot poor/ambiguous results.

5. Do a better job of coding algorithms or incorporating them into more complex analysis pipelines.