Biological Data in Digital Symbol Sequences -- Genomes--Diversity, Size, and Structure -- Proteins and Proteomes -- On the Information Content of Biological Sequences -- Prediction of Molecular Function and Structure -- Machine Learning Foundations: The Probabilistic Framework -- Introduction: Bayesian Modeling -- The Cox-Jaynes Axioms -- Bayesian Inference and Induction -- Model Structures: Graphical Models and Other Tricks -- Probabilistic Modeling and Inference: Examples -- The Simplest Sequence Models -- Statistical Mechanics -- Machine Learning Algorithms -- Dynamic Programming -- Gradient Descent -- EM/GEM Algorithms -- Markov Chain Monte Carlo Methods -- Simulated Annealing -- Evolutionary and Genetic Algorithms -- Learning Algorithms: Miscellaneous Aspects -- Neural Networks: The Theory -- Universal Approximation Properties -- Priors and Likelihoods -- Learning Algorithms: Backpropagation -- Neural Networks: Applications -- Sequence Encoding and Output Interpretation -- Prediction of Protein Secondary Structure -- Prediction of Signal Peptides and Their Cleavage Sites -- Applications for DNA and RNA Nucleotide Sequences -- Hidden Markov Models: The Theory -- Prior Information and Initialization -- Likelihood and Basic Algorithms -- Learning Algorithms -- Applications of HMMs: General Aspects -- Hidden Markov Models: Applications -- Protein Applications -- DNA and RNA Applications -- Conclusion: Advantages and Limitations of HMMs -- Hybrid Systems: Hidden Markov Models and Neural Networks -- Introduction to Hybrid Models.