A First Course In Machine Learning
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A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail.
Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.
Written for undergraduate and graduate students, A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering, and projection. Topics include linear modeling, making predictions, vector/matrix notation, and nonlinear response from a linear model.
Whether you are transitioning a classroom course to a hybrid model, developing virtual labs, or launching a fully online program, MathWorks can help you foster active learning no matter where it takes place.
Above is a graphic to better understand these three categories and their relationship with each other. Artificial Intelligence mirrors the intellectual abilities and behavioral patterns of humans. Machine Learning is the process through which a machine learns from data without the benefit of a detailed set of rules. Deep Learning is the method of achieving machine learning that is modeled on the human neural network.
This book will be excellent for those that want to build a strong mathematical foundation for their knowledge on the main machine learning techniques, and at the same time get python recipes on how to perform the analyses for worked examples.\" - Victor Moreno, ISCB News, December 2020
A Tour of Data Science: Learn R and Python in Parallel by Nailong Zhang covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book.
Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
Simon Rogers is a lecturer in the School of Computing Science at the University of Glasgow, where he teaches a masters-level machine learning course on which this book is based. Dr. Rogers is an active researcher in machine learning, particularly applied to problems in computational biology. His research interests include the analysis of metabolomic data and the application of probabilistic machine learning techniques in the field of human-computer interaction.
Meeting Times: Mondays and Wednesdays, 2:00-3:15Location: 201 Engr ScienceInstructor: Edmond ChowE-mail: Office hours: TBDTA: Shikhar ShahE-mail: email@example.comOffice hours: TBDCourse DescriptionThis course is designed for students who want to better understand machinelearning methods through looking at pseudocode and programming them.We will implement machine learning algorithms and experiment with howthey work on different types of data.In addition, this course will provide and strengthen the mathematical backgroundneeded to develop intuition for deeply understanding machine learningalgorithms. Statistics, numerical optimization, and linear algebra willbe used at a fundamental level necessary for quickly grasping andextending the main ideas behind machine learning.This course is of interest to students who wish to pursue researchin machine learning, as well as students wishing to pursue careersas algorithm designers and software framework developers for machinelearning.This course is intended as a second course in machine learning in thesense that it is more abstract than a first course and applicationsof machine learning will not be discussed. However, the course isself-contained and it would be possible to take this course as afirst course, especially for those who want more exposure to relevantmathematical ideas before taking other machine learning courses.Prerequisites
Understand the concept of learning in computer and science.Understand the difference between supervised and unsupervised learning.Understand the difference between machine lea ring and deep learning.Design and evaluate machine and deep learning algorithms.
This course covers the analysis of data for making decisions with applications to electronic commerce, AI and intelligent agents, business analytics, and personalized medicine. The focus will be on learning good and automated decision policies, inferring causal effects of potential decisions, and interactive and intelligent systems that learn through acting and act to learn. Topics include A/B testing, sequential decision making and bandits, decision theory, risk minimization and generalization, Markov decision processes, reinforcement learning, analysis of observational data, instrumental variable analysis, and algorithmic fairness of personalized decision policies. Students are expected to have taken a first course in machine learning and have working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python.
Chatbots, spam filtering, ad serving, search engines, and fraud detection are among just a few examples of how machine learning models underpin everyday life. Machine learning lets us find patterns and create mathematical models for things that would sometimes be impossible for humans to do.
Unlike data science courses, which contain topics like exploratory data analysis, statistics, communication, and visualization techniques, machine learning courses focus on teaching only the machine learning algorithms, how they work mathematically, and how to utilize them in a programming language.
This book has detailed, straightforward explanations and examples to boost your overall mathematical intuition for many fundamental machine learning techniques. This book is more on the theory side of things, but it does contain many exercises and examples using the R programming language.
A good complement to the previous book since this text focuses more on applying machine learning using Python. Together with any of the courses below, this book will reinforce your programming skills and immediately show you how to apply machine learning to projects.
Also taught by Andrew Ng, this specialization is a more advanced course series for anyone interested in learning about neural networks and Deep Learning, and how they solve many problems.
To understand the algorithms presented in this course, you should already be familiar with Linear Algebra and machine learning in general. If you need some suggestions for picking up the math required, see the Learning Guide towards the end of this article.
This is the best option in this list if you have tinkered with ML but are looking to cover all your bases. The course discusses many nuances of machine learning that may otherwise take hundreds of hours to learn serendipitously.
Another beginner course, but this one focuses solely on the most fundamental machine learning algorithms. The instructor, slide animations, and explanation of the algorithms combine very nicely to give you an intuitive feel for the basics.
This is another advanced series of courses that casts a very wide net. If you are interested in covering as many machine learning techniques as possible, this Specialization is the key to a balanced and extensive online curriculum.
The instruction in this course is fantastic: extremely well-presented and concise. Due to its advanced nature, you will need more math than any other courses listed so far. If you have already taken a beginner course and brushed up on linear algebra and calculus, this is a good choice to fill out the rest of your machine learning expertise.
This course is excellent if you're a programmer who wants to learn and apply ML techniques, but I find there is one drawback: they teach machine learning through the use of their open-source library (called fastai), which is a layer over other machine learning libraries, like PyTorch.
These are the general components of being able to understand how machine learning works under the hood. Many beginner courses usually ask for at least some programming and familiarity with linear algebra basics, such as vectors, matrices, and their notation.
The first course in this list, Machine Learning by Andrew Ng, contains refreshers on most of the math you'll need, but it might be challenging to learn machine learning and Linear Algebra if you haven't taken Linear Algebra before at the same time.
Andreas Lindholm is a machine learning research engineer at Annotell, Gothenburg, working with data annotation and data quality questions for autonomous driving. He received his MSc degree in 2013 from Linköping University (including studies at ETH Zürich and UC Santa Barbara). He received his PhD degree in 2018 from the Department of Information Technology, Uppsala University. At the time of writing this book he was a postdoctoral researcher at the same department. Throughout his entire academic career he has had a particular interest in teaching applied mathematical subjects. 59ce067264