Opportunities

This introductory-level course in supervised learning covers regression and classification techniques, including linear and polynomial regression, logistic regression, cross-validation, and more. It also touches on some unsupervised learning methods like principal components and clustering. The course emphasizes practical understanding without heavy math, using R for computing and providing detailed tutorials on its usage. Explore modern data analysis essentials with us.

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. We also offer a separate version of the course called Statistical Learning with Python – the chapter lectures are the same, but the lab lectures and computing are done using Python.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani, with Applications in R (second addition) by James, Witten, Hastie and Tibshirani (Springer, 2021). The pdf for this book is available for free on the book website.

Prerequisites: Foundational knowledge in statistics, linear algebra, and computing is recommended.

Instructors: Learn from Trevor Hastie, Professor of Statistics at Stanford University, and Robert Tibshirani, Professor in Health Research and Policy and Statistics at Stanford University.

This course delves into the fundamentals of R, a popular programming language and software environment extensively utilized by data analysts, statisticians, and data scientists for statistical computing and graphics across the globe. It provides an introductory journey into R, encompassing installation procedures and foundational statistical operations. The course equips you with the skills to handle variables and external datasets, create functions, and offers insights from one of the R language’s co-creators, Robert Gentleman. Explore the world of R and enhance your statistical computing capabilities with this comprehensive course.

This course equips you with core competencies in R, covering data types, variables, vectors, matrices, lists, data frames, data import, logical statements, loops, functions, data plotting, visualization, and basic statistical functions. Prior experience in a scientific or engineering discipline is helpful but not mandatory, making it accessible to learners with basic computer skills. Join us to enhance your proficiency in R and excel in statistical computing and data analysis.

Computer Science 101 is a stand-alone course that teaches the basic ideas of computer science to an audience with zero previous experience. Computers may look very complicated, but in reality they only work on a few simple circuits. The course demystifies and brings these patterns to life, which is useful for anyone using computers today.

Course participants play and experiment with short pieces of “computer code” to understand the power and limitations of computers. Everything works within the browser, so there is no additional software to download or install. CS101 also provides general information about computers today: what is a computer, what is hardware, what is software, what is the Internet. Anyone who knows how to use a web browser can be successful with this course. No previous computer science experience is required.

Topics

• The nature of computers and code, what they can and cannot do
• How computer hardware works: chips, cpu, memory, disk
• Necessary jargon: bits, bytes, megabytes, gigabytes
• How software works: what is a program, what is “running”
• How digital images work
• Computer code: loops and logic
• Big ideas: abstraction, logic, bugs
• How structured data works
• How the internet works: ip address, routing, ethernet, wi-fi
• Computer security: viruses, trojans, and passwords, oh my!
• Analog vs. digital
• Digital media, images, sounds, video, compression

Instructor

Nick Parlante, Senior Lecturer, Computer Science

The Machine Learning Specialization is a foundational online program created in collaboration between Stanford Online and DeepLearning.AI. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization, consisting of 3 courses, is an updated and expanded version of Andrew Ng’s original Machine Learning course. It offers a comprehensive introduction to modern machine learning, covering supervised learning (including multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (including clustering, dimensionality reduction, and recommender systems), and best practices in artificial intelligence and machine learning innovation (such as model evaluation, tuning, and data-centric performance improvement).

This course requires no prior knowledge, making it accessible to beginners. It covers core competencies including multiple linear regression, logistic regression, neural networks, decision trees, clustering, dimensionality reduction, recommender systems, and best practices for evaluating and tuning models, as well as adopting a data-centric approach to enhance performance. Get started on your machine learning journey with this accessible and comprehensive course.

This course begins by introducing modern distributed file systems and MapReduce, with a focus on what distinguishes effective MapReduce algorithms for handling large datasets. The remainder of the course delves into algorithms for extracting valuable models and insights from these vast datasets. Topics include Google’s PageRank algorithm for assessing web page importance and its various extensions, locality-sensitive hashing for identifying similar items in massive datasets, and efficient dimensionality reduction techniques for large, sparse matrices. The course also explores a range of other large-scale algorithms, as detailed in the syllabus.

The course lasts for 7 weeks. Before taking it, a course in database systems is recommended, as is a basic course on algorithms and data structures.