Mastering Artificial Intelligence

Chapter 1:Introduction

• In this course you will learn to implement mathematical ideas in machine learning. You will investigate the process of learning and understand the application of various learning algorithms.
• Prerequisites:
  The courses assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals.

Chapter 2: Linear Models

• Understand linear approximation and modelling of problems and develop linear models

Chapter 3: Dimensionality Reduction

• Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models.

Chapter 4:SVM

• Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks.

Chapter 5:Nearest Neighbours, K-means, and Gaussian Mixture Models

• Review pattern recognition ideas with distance and cluster based models to understand similarity measures and grouping criteria.

Chapter 6:Naive Bayes and Decision Trees

• Dive into applications of bayes theorem and the use of decision criteria when learning from data.

CHAPTER 7:Search

• Look at search from the perspective of graphs, trees and heuristic based optimizations.

CHAPTER 8:Logic and Planning

• Discover ways to encode logic and develop agents that plan actions in an environment.

CHAPTER 9:Reinforcement Learning and Hidden Markov Models

• Engineering agents that learn from a sequence of actions using rewards and penalties.
  

CHAPTER 10:Q-Learning and Policy gradient

• Operate in a stateful world over value and policy approximations tasks