Top 7 FREE Artificial Intelligence Courses from the Ivy League Universities | by Roman Orac | Towards Data Science

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These days it feels like every week comes with a new AI course. With such volume, we need to be really selective with our time, energy and focus. A simple but effective strategy is to attend the courses from the best minds in the field.
Use your time effectively and attend the courses from the best minds in the field.
With the help of my fellow Data Scientists, we curated a list of the top 7 Artificial Intelligence courses from the Ivy League Universities. The course had to be free to be included in the list.
I haven’t attended all the courses on the list but I got high praise from my colleagues. Next on my course list, I wish to attend is the Reinforcement Learning course.
Ivy League is a group of eight private universities: Harvard, Yale, Princeton, Brown, Dartmouth, Columbia, Cornell, and the University of Pennsylvania.
While Stanford and MIT are clearly prestigious schools, they are not Ivy League schools simply because they are not members of the Ivy League.
Here are a few links that might interest you:
- Labeling and Data Engineering for Conversational AI and Analytics- Data Science for Business Leaders [Course]- Intro to Machine Learning with PyTorch [Course]- Become a Growth Product Manager [Course]- Deep Learning (Adaptive Computation and ML series) [Ebook]- Free skill tests for Data Scientists & Machine Learning Engineers
Some of the links above are affiliate links and if you go through them to make a purchase I’ll earn a commission. Keep in mind that I link courses because of their quality and not because of the commission I receive from your purchases.
In case you’ve missed my other two articles related to this topic:
towardsdatascience.com
towardsdatascience.com
University: Georgia TechInstructor: Prof. Charles Isbell
www.udacity.com
Reinforcement Learning is one of the hottest topics in Machine Learning. You should take this course if you have a desire to engage with it from a theoretical perspective.
In this course, you will explore automated decision-making from a Computer Science perspective through a combination of classic papers and more recent work. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.
University: MITInstructor: Prof. Berthold Horn
ocw.mit.edu
This MIT course provides an intensive introduction to the process of generating a symbolic description of an environment from an image.
In lectures, you will learn the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps.
Further topics include photogrammetry, object representation alignment, analog VLSI and Computational Vision. Applications to robotics and intelligent machine interaction are discussed.
University: MITInstructor: Prof. Philippe Rigollet
ocw.mit.edu
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments.
The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.
University: HarvardXInstructor: Prof. Rafael Irizarry
www.edx.org
In this course, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007–2008.
Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.
This course will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance.
Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for Data Scientists.
University: ColumbiaXInstructor: Ansaf Salleb-Aouissi, Ph.D.
www.edx.org
What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?
They are all complex real-world problems being solved with applications of intelligence (AI).
This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.
You will gain hands-on experience by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to Machine Learning includes work on linear regression.
University: ColumbiaInstructor: John W. Paisley, Ph.D.
www.edx.org
Machine Learning is the basis for the most exciting careers in Data Analysis today. You’ll learn the models and methods and apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams and plotting the path of movie zombies.
Major perspectives covered include:
probabilistic versus non-probabilistic modeling
supervised versus unsupervised learning
Topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.
Methods include linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.
University: PrincetonInstructor: Kevin Wayne, Ph.D.
www.coursera.org
This course is for you if you would like to improve your programming skills. While it is not strictly related to Machine Learning, you’ll build strong Computer Science foundations.
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.
Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
University: StandfordInstructor: Prof. Andrew Ng
www.coursera.org
Andrew Ng’s Coursera course is my favorite Machine Learning Course. It is the first course I took in the field. You’ll build strong Machine Learning foundations by listening to this course
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots, text understanding, computer vision and other areas.
University: Johns HopkinsInstructor: Prof. Avi Rubin
hopkinspokercourse.com
Johns Hopkins Poker Course is not strictly related to Machine Learning, but still kinda related as it deals with statistics. This is a fun way to learn about math and combinatorics.
This intersession course aims to take students from novices who may know nothing about poker to above-average players, in two weeks. The course will utilize hand examples and discussions of common poker situations to study the fundamentals of the game.
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