Description
Book SynopsisThis textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment.
This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.
Table of Contents1. Basics of Modern Computer Systems (Unix/Linux Centric)
a. Computer Hardware Basics
b. Operating System
c. Files & Directories
d. Programs and Shells
e. Programming Languages
f. Troubleshooting Computer Problems (How to Google issues.)
2. The Python Programming Language ( A tool to enter the world of Machine Learning.)
a. Short History
b. The Python Interpreter
c. Basic Syntax
d. Working with Popular Libraries (Modules)
e. Optimization
f. Advanced Concepts
g. Introduction to ML libraries
3. Basic Math
a. Overview
b. Linear Algebra Basics
c. Calculus Basics
d. Probability
e. Use cases of above three in ML
4. Introduction to the World of Bioinformatics
a. Laying the Foundation
b. A Brief History
c. Goals of Bioinformatics
d. Genomes, Genes, Sequences
e. Protein and structures
f. Databases
g. Bioinformatics Tools
5. Introduction to Artificial Intelligence & ML
a. Machine Learning
i. History of Machine Learning
ii. Why Machine Learning?
iii. Machine Learning Approaches
iv. Machine Learning Applications
b. Artificial Intelligence
i. What is AI?
ii. Basic Principles
iii. General applications of Artificial Intelligence
6. Fundamentals of ML
a. Types of learning
i. Supervised
ii. Unsupervised
b. Popular Algorithms
c. Deep learning and related concepts
d. Model Training and Testing
e. Summary
7. Applications in the field of Bioinformatics
a. In Systems Biology
b. Biological Data Analysis and Predictions
c. In Healthcare, Diagnosis and Treatment
8. Future Prospects
9. Further Readings
