The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
The course requires a functional knowledge of high-school-level linear algebra, calculus, probability, statistics, and Python programming.
TBD
10 weeks, online
5-10 hours/week
Our participants tell us that taking this program together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for Machine Learning: Fundamentals and Algorithms starting on TBD .
We’ve sent you an email with enrollment next steps. If you’re ready to enroll now, click the button below.
Have questions? Email us at group-enrollments@emeritus.org.With the paradigm shift in technology trending hard in the direction of machine learning and artificial intelligence, the skills of future-ready technologists, analysts, engineers and data managers also must shift, expand and advance. Machine Learning: Fundamentals and Algorithms, an online program offered by Carnegie Mellon University’s School of Computer Science Executive Education, provides you with the technical knowledge and analytical methods that will prepare you for the next generation of innovation.
Source: U.S. News & World Report
This 10-week online program is designed to provide software engineers, data analytics professionals and technical data managers with a skillset focused on fundamental machine learning methods. Participants who complete the program will be prepared to do the following:
This program is designed for participants who have experience with Python programming and want to learn more about the underlying mathematics behind machine learning algorithms. This program is most suitable for the following:
Engineers in IT products and services, healthcare, or banking and financial services who want hands-on instruction in the tools and techniques of machine learning.
Representative roles include:
Data Analytics Professionals in the banking and financial services industry, or IT products and services, with responsibility for publishing reports, innovating, and working with analytics in a data-dense environment. This program will be especially relevant for analysts seeking to implement machine learning into projects or to upgrade from spreadsheet-based analysis to more powerful programmatic models of data analysis.
Representative roles include:
Technical Managers/Directors of Data Functions leading a team of coders in banking and financial services, IT, healthcare, retail, logistics, or industrial goods who want to create enterprise value and gain hands-on skills in machine learning technology with the goal of solving business pain points.
Representative roles include:
PREREQUISITES: Participants will be expected to write their own code from scratch, therefore prior experience with coding is required. Prior to enrolling, we strongly encourage you to complete the provided self-assessment exercises designed to evaluate your competency with mathematics content, the Python programming language, and Jupyter notebooks. A passing score will indicate your readiness for the rigorous program material, but will not guarantee success. Should you not pass these self-assessments, we recommend you strengthen gaps and weaknesses in your core knowledge and programming skills until you achieve proficiency before program participation.
Organized around 10 modules, this program helps participants broaden and deepen their Python programming skills for machine learning applications. This technical knowledge can be applied to any industry integrating machine learning and artificial intelligence into their digital drivers.
As you begin, you will learn to use a decision tree to make predictions and, given labeled training examples, you will learn a decision tree.
In machine learning, there are fundamental algorithms. In this module, you will learn to use the k-NN algorithm to classify points given a simple dataset and implement a full decision tree for learning and prediction.
Building your skills in Python, you will employ model selection techniques to select k for the k-NN algorithm and implement a grid search to select multiple hyperparameters for a model.
Creating machine learning solutions can require refinement of the inner workings of algorithms, including adapting the k-NN algorithm for classification to regression, adapting decision trees for classification to regression, as well as implementing learning for linear regression using gradient descent.
In this module, you will determine how convexity affects optimization and implement linear regression with optimization by stochastic gradient descent.
Given i.i.d. data and parameters of a logistic regression distribution, you will learn to compute conditional likelihood and learn to implement stochastic gradient descent for binary logistic regression.
As you discover ways to combat overfitting, you will convert a nonlinear dataset to a linear dataset in higher dimensions, manipulate the hyperparameters of L1 and L2 regularization implementations and identify the effects on magnitude and sparsity of parameters.
Combine simpler models as components to build up feed-forward neural network architectures and write mathematical expressions in scalar form defining a feed-forward neural network.
Adding to your deep knowledge of algorithmic applications, you will learn to carry out the backpropagation algorithm on a simple computation graph over scalars and instantiate the backpropagation algorithm for a neural network.
In addition to exploring solutions to practical challenges in this final module, you will learn to implement the k-means algorithm and recognize and explain challenges in selecting the number of clusters.
As you begin, you will learn to use a decision tree to make predictions and, given labeled training examples, you will learn a decision tree.
Given i.i.d. data and parameters of a logistic regression distribution, you will learn to compute conditional likelihood and learn to implement stochastic gradient descent for binary logistic regression.
In machine learning, there are fundamental algorithms. In this module, you will learn to use the k-NN algorithm to classify points given a simple dataset and implement a full decision tree for learning and prediction.
As you discover ways to combat overfitting, you will convert a nonlinear dataset to a linear dataset in higher dimensions, manipulate the hyperparameters of L1 and L2 regularization implementations and identify the effects on magnitude and sparsity of parameters.
Building your skills in Python, you will employ model selection techniques to select k for the k-NN algorithm and implement a grid search to select multiple hyperparameters for a model.
Combine simpler models as components to build up feed-forward neural network architectures and write mathematical expressions in scalar form defining a feed-forward neural network.
Creating machine learning solutions can require refinement of the inner workings of algorithms, including adapting the k-NN algorithm for classification to regression, adapting decision trees for classification to regression, as well as implementing learning for linear regression using gradient descent.
Adding to your deep knowledge of algorithmic applications, you will learn to carry out the backpropagation algorithm on a simple computation graph over scalars and instantiate the backpropagation algorithm for a neural network.
In this module, you will determine how convexity affects optimization and implement linear regression with optimization by stochastic gradient descent.
In addition to exploring solutions to practical challenges in this final module, you will learn to implement the k-means algorithm and recognize and explain challenges in selecting the number of clusters.
Python Coding Exercise in Each Module
Bite-Sized Learning
Knowledge Checks
Dedicated Program Support Team
Mobile Learning App
Peer Discussion
Patrick Virtue
Matt Gormley
Patrick Virtue
Assistant Teaching Professor, Computer Science and Machine Learning, Carnegie Mellon University
Pat Virtue is an Assistant Teaching Professor in the Computer Science and Machine Learning departments at Carnegie Mellon University. He focuses on teaching techniques for artificial intelligence, machine learning, and computer science. His interests include active learning teaching methods, effective instruction for large classes, building inclusive learning environments, and AI/ML curriculum development. Pat completed his graduate work at UC Berkeley in Electrical Engineering and Computer Sciences, and his undergraduate at the University of Notre Dame. Prior to graduate school, he researched and developed volumetric medical image applications as a software engineer at GE Healthcare.
Matt Gormley
Assistant Teaching Professor, Computer Science and Machine Learning, Carnegie Mellon University
As Assistant Teaching Professor in the Machine Learning Department at CMU, Dr. Gormley regularly teaches Introduction to Machine Learning to more than 400 students, one of the largest courses offered at CMU. His research focuses on machine learning for natural language processing. His interests include global optimization, learning under approximations, hybrids of graphical models and neural networks and applications where supervised resources are scarce.
Dr. Gormley earned a BS in Computer Science and Cognitive Science from CMU and an MSE and PhD in Computer Science from Johns Hopkins University.
Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University’s School of Computer Science Executive Education. This is a training program and it is not eligible for academic credit.
Download BrochureYour digital certificate will be issued in your legal name and emailed to you at no additional cost, upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Carnegie Mellon University’s School of Computer Science Executive Education.
At Carnegie Mellon’s Executive Education Program in the School of Computer Science, we provide organizations and people access to the skills and tools necessary to solve real world technical problems by equipping the next generation of technology leaders with the experience, insights and novel solutions developed by our community of computer science experts. From custom training programs to online individualized learning, our cutting-edge programming — backed by faculty who pioneered the field — takes your skillset to the next level, giving you the tools to tackle your company’s next great technological challenge.