Machine learning for Engineers .

I Basics
1. Introduction
2. A Gentle Introduction through Linear Regression
3. Probabilistic Models for Learning

II Supervised Learning
4. Classification
5. Statistical Learning Theory
6. Unsupervised Learning
IV Advanced Modelling and Inference
7. Probabilistic Graphical Models
8. Approximate Inference and Learning
Appendices

Artificial Intelligence a vital responsibility in the ethics and governance of AI, comprising their work, advocacy, and choice of employment. Artificial Intelligence which is developing in the prevailing world,where most of the things are driven by technology and data, the need arises to automate any system or process to perform complex tasks and function automatically in order to deliver optimal productivity.
AI problems are usually inaccurate and the models proposed are often too complicated to be proved by proper arguments. The only way to comprehend and appraise a theory is by perceiving what comes next. To evaluate our discrepancies and disagreements, we write programs that are expected to reflect our theories. If these programs perform, our theories are not proved now, we gain some insight of how they perfrom. When the program don't yield expected results, we become incompetent to program the theories, we understand what we need to re-define. This becomes valid because of the "level" problem. When the theory defines what to do at a high level, we need to communicate the machine what tp dp at a low level.