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- Decoding AI, what is it all about?
- Use cases in various industries
- Introduction to Machine Learning and Deep Learning
- Innovations in AI and ML
- Applications of AI
- How to develop AI products
- Societal impact of AI
- Understand linear approximation and modelling of problems and develop linear models
- Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.
- Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks.
- Review pattern recognition ideas with distance and cluster based models to understand similarity measures and grouping criteria.
- Dive into applications of bayes theorem and the use of decision criteria when learning from data.
- Look at search from the perspective of graphs, trees and heuristic based optimizations.
- Discover ways to encode logic and develop agents that plan actions in an environment.
- Engineering agents that learn from a sequence of actions using rewards and penalties.
- Operate in a stateful world over value and policy approximations tasks