Preface
[ 2 ]
Chapter 5, Building Recommender Systems, illustrates algorithms used to build
recommendation engines. You will learn how to apply these algorithms to collaborative
filtering and movie recommendations.
Chapter 6, Logic Programming, covers the building blocks of logic programming. We will
see various applications, including expression matching, parsing family trees, and solving
puzzles.
Chapter 7, Heuristic Search Techniques, shows heuristic search techniques that are used to
search the solution space. We will learn about various applications such as simulated
annealing, region coloring, and maze solving.
Chapter 8, Genetic Algorithms, covers evolutionary algorithms and genetic programming.
We will learn about various concepts such as crossover, mutation, and fitness functions. We
will then use these concepts to solve the symbol regression problem and build an intelligent
robot controller.
Chapter 9, Building Games with Artificial Intelligence, teaches you how to build games with
artificial intelligence. We will learn how to build various games including Tic Tac Toe,
Connect Four, and Hexapawn.
Chapter 10, Natural Language Processing, covers techniques used to analyze text data
including tokenization, stemming, bag of words, and so on. We will learn how to use these
techniques to do sentiment analysis and topic modeling.
Chapter 11, Probabilistic Reasoning for Sequential Data, shows you techniques used to analyze
time series and sequential data including Hidden Markov models and Conditional Random
Fields. We will learn how to apply these techniques to text sequence analysis and stock
market predictions.
Chapter 12, Building A Speech Recognizer, demonstrates algorithms used to analyze speech
data. We will learn how to build speech recognition systems.
Chapter 13, Object Detection and Tracking, It covers algorithms related to object detection
and tracking in live video. We will learn about various techniques including optical flow,
face tracking, and eye tracking.
Chapter 14, Artificial Neural Networks, covers algorithms used to build neural networks. We
will learn how to build an Optical Character Recognition system using neural networks.
Chapter 15, Reinforcement Learning, teaches the techniques used to build reinforcement
learning systems. We will learn how to build learning agents that can learn from interacting
with the environment.
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