Applications of Hidden Markov Chains: Applications

In hidden Markov chains, the system's behavior depends on latent (or hidden) variables. This has a lot of applications in contemporary AI. For now, focus on grasping the high-level themes and ideas. If the subject interests you, you can dive deeper into technical details. The examples are particularly instructive.

Applications

A profile HMM modelling a multiple sequence alignment of proteins in Pfam

HMMs can be applied in many fields where the goal is to recover a data sequence that is not immediately observable (but other data that depend on the sequence are). Applications include:

Computational finance
Single-molecule kinetic analysis
Neuroscience
Cryptanalysis
Speech recognition, including Siri
Speech synthesis
Part-of-speech tagging
Document separation in scanning solutions
Machine translation
Partial discharge
Gene prediction
Handwriting recognition
Alignment of bio-sequences
Time series analysis
Activity recognition
Protein folding
Sequence classification
Metamorphic virus detection
Sequence motif discovery (DNA and proteins)
DNA hybridization kinetics
Chromatin state discovery
Transportation forecasting
Solar irradiance variability

Course Syllabus

Course Syllabus

Unit 1: What Is Artificial Intelligence?

1.1: The Turing Test

The Turing Test for Intelligence

Why the Turing Test Is Important

1.2: The Four Types of AI

Is Intelligence How You Think or the Output of Thinking?

Unit 1 Assessment

Unit 2: Agent-Based Approach to AI

2.1: Introduction to Agent-Based AI

Agents, Agent Types, and Their Capabilities

2.2: Analyzing Environmental Characteristics

Properties of Problem Environments and How to Analyze Them

Unit 2 Assessment

Unit 3: Machine Learning and Its Importance

3.1: Learning in AI and Agents

Supervised, Unsupervised, and Reinforcement ML

3.2: Applications of ML in Neural Networks

Newer Machine Learning Models and Applications

Unit 4: Machine Learning Algorithms

4.1: Classification Algorithms

Classification versus Regression

Importance of Classification and Regression in Machine Learning

Classification Using K-nearest Neighbors Algorithm

4.2: Classification Algorithm Performance

False Positives / False Negatives / Confusion Matrix

Precision and Recall Calculations from Confusion Matrix

Linear Regression – How It Works

4.3: Linear Regression Algorithms

Metrics for Linear Regression Effectiveness: R-squared, MSE and RSE

Lasso and Ridge Regression

Improving Linear Regression by Reducing Residual Errors

4.4: Other Supervised ML Classification Algorithms

Classification Using Decision Trees

Classification Using Logistic Regression

Applying Bayes' Theorem in Machine Learning

4.5: Unsupervised Learning and Reinforcement Learning

Unlabelled Data and Unsupervised Machine Learning

Principles and Applications of Reinforcement Learning

4.6: ML Using Neural Networks

Introduction to Neural Networks Basics

Neural Networks: Types and Applications

Unit 5: Problem-Solving Methods in AI

5.1: Integrating ML Skills

Applying Classification to Determine Insurability

How Regression Is Applied in Contemporary Computing

Using Neural Networks in Cancer Detection

5.2: General AI Problem-Solver Architecture

Characteristics of General Problem-Solver

5.3: Designing a General Problem-Solving Agent

How GPS Is Used

Computational Tractability of GPS

Unit 6: Search Algorithms

6.1: Uninformed Search Algorithms

Uninformed or Brute Force Search

Depth First Search Algorithm

Breadth First Search Algorithm

Uniform Cost Search Algorithm

6.2: Heuristic Search Algorithms

Heuristics and Using Them to Improve Search

Overview of A* Search and Analysis of Performance

Unit 7: Iterative Improvement Algorithms

7.1: Using Iterative Improvement to Solve Problems

Iterative Improvement Algorithms and Hill-Climbing

Constraint Satisfaction Problems and Their Importance

7.2: Improving Algorithm Efficiency

How Simulated Annealing Improves Hill-Climbing

Improving Mediocre Solutions Using Genetic Algorithms

Unit 8: Game-Playing Models

8.1: Game Trees and the Minimax Algorithm

Principles of Game Trees and How to Create One

Using the Minimax Algorithm in Adversarial Games

Assumptions Underlying Minimax Approach

8.2: Game-Playing Strategies

The Alpha Beta Pruning Algorithm

Tackling Multi-person Games

Unit 9: Natural Language Processing

9.1: Foundations of NLP

NLP Overview, Challenges, and Applications