Semester-Plus-C04-Sem 2

Semester-Plus-C04-Sem 2
Author: Wilson Rita
Publisher: Saraswati House Pvt Ltd
Total Pages: 312
Release:
Genre: Language Arts & Disciplines
ISBN: 9386137305

SemesterPlus is an engaging and interactive series of 10 books covering English, Maths, Science/EVS, Social Studies and General Knowledge. The entire syllabi is judiciously and evenly distributed into semesters in each grade. The series combines theoretical learning with a practical, participative and hands-on approach.

Semester-Plus-C04-Sem 1

Semester-Plus-C04-Sem 1
Author: Wilson Rita
Publisher: Saraswati House Pvt Ltd
Total Pages: 312
Release:
Genre: Language Arts & Disciplines
ISBN: 9386137291

SemesterPlus is an engaging and interactive series of 10 books covering English, Maths, Science/EVS, Social Studies and General Knowledge. The entire syllabi is judiciously and evenly distributed into semesters in each grade. The series combines theoretical learning with a practical, participative and hands-on approach.

Probability and Statistics

Probability and Statistics
Author: Michael J. Evans
Publisher: Macmillan
Total Pages: 704
Release: 2004
Genre: Mathematics
ISBN: 9780716747420

Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.

A Book of Abstract Algebra

A Book of Abstract Algebra
Author: Charles C Pinter
Publisher: Courier Corporation
Total Pages: 402
Release: 2010-01-14
Genre: Mathematics
ISBN: 0486474178

Accessible but rigorous, this outstanding text encompasses all of the topics covered by a typical course in elementary abstract algebra. Its easy-to-read treatment offers an intuitive approach, featuring informal discussions followed by thematically arranged exercises. This second edition features additional exercises to improve student familiarity with applications. 1990 edition.

All of Statistics

All of Statistics
Author: Larry Wasserman
Publisher: Springer Science & Business Media
Total Pages: 446
Release: 2013-12-11
Genre: Mathematics
ISBN: 0387217363

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Mathematics for Machine Learning

Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Introduction to Applied Linear Algebra

Introduction to Applied Linear Algebra
Author: Stephen Boyd
Publisher: Cambridge University Press
Total Pages: 477
Release: 2018-06-07
Genre: Business & Economics
ISBN: 1316518965

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Mathematics for Computer Science

Mathematics for Computer Science
Author: Eric Lehman
Publisher:
Total Pages: 988
Release: 2017-03-08
Genre: Business & Economics
ISBN: 9789888407064

This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

Exercises And Problems In Linear Algebra

Exercises And Problems In Linear Algebra
Author: John M Erdman
Publisher: World Scientific
Total Pages: 220
Release: 2020-09-28
Genre: Mathematics
ISBN: 9811220425

This book contains an extensive collection of exercises and problems that address relevant topics in linear algebra. Topics that the author finds missing or inadequately covered in most existing books are also included. The exercises will be both interesting and helpful to an average student. Some are fairly routine calculations, while others require serious thought.The format of the questions makes them suitable for teachers to use in quizzes and assigned homework. Some of the problems may provide excellent topics for presentation and discussions. Furthermore, answers are given for all odd-numbered exercises which will be extremely useful for self-directed learners. In each chapter, there is a short background section which includes important definitions and statements of theorems to provide context for the following exercises and problems.