# Machine Learning Textbook

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A Machine Learning Textbook is an AI textbook for ML concepts.

**Example(s):**- (Murphy, 2012).
- (Bishop, 2006).
- (Mitchell, 1997)

**Counter-Example(s):****See:**ML Course, ML Scholarly Paper, ML Tutorial, Machine Learning Education.

## References

### 2012

- (Murphy, 2012) ⇒ Kevin P. Murphy. (2012). “Machine Learning: A Probabilistic Perspective.” The MIT Press. ISBN:0262018020, 9780262018029
- QUOTE: … The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

### 2006

- (Bishop, 2006) ⇒ Christopher M. Bishop. (2006). “Pattern Recognition and Machine Learning. Springer, Information Science and Statistics.

### 1997

- (Mitchell, 1997) ⇒ Tom M. Mitchell. (1997). “Machine Learning." McGraw-Hill. ISBN:0070428077