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
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