Ethem Alpaydin

An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning. Most curricula have students take an undergraduate course on probability and statistics before turning to machine learning. In this innovative textbook, Ethem Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın accessibly connects machine learning to its roots in probability and statistics, starting with the basics of random experiments and probabilities and eventually moving to complex topics such as artificial neural networks. With a practical emphasis and learn-by-doing approach, this unique text offers comprehensive coverage of the elements fundamental to an empirical understanding of machine learning in a data science context. Consolidates foundational knowledge and key techniques needed for modern data scienceEmphasizes hands-on learningCovers mathematical fundamentals of probability and statistics and ML basicsSuits undergraduates as well as self-learners with basic programming experienceIncludes slides, solutions, and code

Koneoppiminen on eräänlaista tekoälyä, jonka ansiosta tietokoneet oppivat eristämään laajasta aineistosta uutta tietoa ja toimimaan sen perusteella. Tietokoneiden laskentatehon kasvun seurauksena koneoppimisen sovellukset ulottuvat nykyään puheentunnistuksesta itseohjautuviin autoihin. Koneoppiminen on niinkin arkisilta vaikuttavien asioiden kuin elokuvien suosittelun ja sosiaalisen median toimintojen perusta. Koneoppiminen esíttelee aiheensa laajasti ja helppolukuisesti monin esimerkein. Ethem Alpaydin on tietotekniikan professori Istanbulin Bogaziçi-yliopistossa ja laajasti käytetyn koneoppimisen oppikirjan tekijä.

MIT presents a concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars. No in-depth knowledge of math or programming required! Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don’t yet use every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin explains that as Big Data has grown, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He covers: • The evolution of machine learning • Important learning algorithms and example applications • Using machine learning algorithms for pattern recognition • Artificial neural networks inspired by the human brain • Algorithms that learn associations between instances • Reinforcement learning • Transparency, explainability, and fairness in machine learning • The ethical and legal implicates of data-based decision making A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming—making it accessible for everyday readers and easily adoptable for classroom syllabi.