Introduction to Artificial Intelligence

Chapter 1: Introduction to AI

This chapter introduces some of the basic concepts of AI. You would see that AI Algorithms takes an Input Array & produces an Output Array. Problems to be solved by AI are modeled to this form.

Chapter 3: Distance Metrics

This chapter shows how data can be compared in much the same way as we plot distance between two points on a Map. AI often work with numeric Arrays.

Chapter 5: K-Means

This chapter shows how data can be grouped into similar Clusters. K means is an algorithm that can be used to group data into commonality.

Chapter 7: Towards Machine Learning

This chapter is more about algorithms that can be trained to analyze data and produce better results. Most AI algorithms use a vector of weighted values to transform the input vector into a desired output vector.

Chapter 9: Discrete Optimization

This chapter shows how to optimize data that is categorical rather than numeric.

Chapter 2: Normalizing Data

This chapter shows how raw data is typically prepared for many AI Algorithms. Data is presented to an AI Algorithm in the form of an Input Array.

Chapter 4: Random Numbers

This chapter discusses about uniform and random numbers. Sometimes AI algorithm call for each Random Number to have an equal probability.

Chapter 6: Error Calculation

This chapter describes how the results of an AI Algorithm can be evaluated. Error calculation is how we determine the effectiveness of an Algorithm.

Chapter 8: Optimization Algorithms

This chapter is about extension of Algorithms which include simulated Annealing and Nelder Mead to quickly optimize the weight of an AI Model.

Chapter 10: Linear Regression

This chapter shows how linear and non-linear equations can be used to learn trends and make predictions. This chapter introduces simple linear regression and shows how to use it to fit data to a linear model.