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.
December 8, 2022
10 weeks, online
5-10 hours/week
US$2,500 or get US$225 off with a referral
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 December 8, 2022 .
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.
After reviewing the information on the program landing page, we recommend you submit the short form above to gain access to the program brochure, which includes more in-depth information. If you still have questions on whether this program is a good fit for you, please email learner.success@emeritus.org, and a dedicated program advisor will follow-up with you very shortly.
Some programs do have prerequisites, particularly the more technical ones. This information will be noted on the program landing page, as well as in the program brochure. If you are uncertain about program prerequisites and your capabilities, please email us at learner.success@emeritus.org for assistance.
Note that, unless otherwise stated on the program web page, all programs are taught in English and proficiency in English is required.
More than 50 percent of our participants are from outside the United States. Class profiles vary from one cohort to the next, but, generally, our online certificates draw a highly diverse audience in terms of professional experience, industry, and geography — leading to a very rich peer learning and networking experience.
Check back to this program web page or email us at learner.success@emeritus.org to inquire if future program dates or the timeline for future offerings have been confirmed yet.
Each program includes an estimated learner effort per week. This is referenced at the top of the program landing page under the Duration section, as well as in the program brochure, which you can obtain by submitting the short form at the top of this web page.
We have designed this program to fit into your current working life as efficiently as possible. Time will be spent among a variety of activities including:
The program is designed to be highly interactive while also allowing time for self-reflection and to demonstrate an understanding of the core topics through various active learning exercises. Please contact us at learner.success@emeritus.org if you need further clarification on program activities.
More than 250,000 professionals globally, across 80 countries, have chosen to advance their skills with Emeritus and its educational learning partners. In fact, 90 percent of the respondents of a recent survey across all our programs said that their learning outcomes were met or exceeded.
A dedicated program support team is available 24/5 (Monday to Friday) to answer questions about the learning platform, technical issues, or anything else that may affect your learning experience.
Peer learning adds substantially to the overall learning experience and is an important part of the program. You can connect and communicate with other participants through our learning platform.
Each program includes an estimated learner effort per week, so you can gauge what will be required before you enroll. This is referenced at the top of the program landing page under the Duration section, as well as in the program brochure, which you can obtain by submitting the short form at the top of this web page. All programs are designed to fit into your working life.
This program is scored as a pass or no-pass ; participants must complete the required activities to pass and obtain the certificate of completion. Some programs include a final project submission or other assignments to obtain passing status. This information will be noted in the program brochure. Please contact us at learner.success@emeritus.org if you need further clarification on any specific program requirements.
Upon successful completion of the program, you will receive a smart digital certificate. The smart digital certificate can be shared with friends, family, schools, or potential employers. You can use it on your cover letter, resume, and/or display it on your LinkedIn profile. The digital certificate will be sent approximately two weeks after the program, once grading is complete.
No, only verified digital certificates will be issued upon successful completion. This allows you to share your credentials on social platforms such as LinkedIn, Facebook, and Twitter.
No, there is no alumni status granted for this program. In some cases, there are credits that count toward a higher level of certification. This information will be clearly noted in the program brochure.
You will have access to the online learning platform and all the videos and program materials for 12 months following the program start date. Access to the learning platform is restricted to registered participants per the terms of agreement.
Participants will need the latest version of their preferred browser to access the learning platform. In addition, Microsoft Office and a PDF viewer are required to access documents, spreadsheets, presentations, PDF files, and transcripts.
Yes, the learning platform is accessed via the internet, and video content is not available for download. However, you can download files of video transcripts, assignment templates, readings, etc. For maximum flexibility, you can access program content from a desktop, laptop, tablet, or mobile device.
Video lectures must be streamed via the internet, and any livestream webinars and office hours will require an internet connection. However, these sessions are always recorded, so you may view them later.
Yes, you can register up until seven days past the published start date of the program without missing any of the core program material or learnings.
The program fee is noted at the top of this program web page and usually referenced in the program brochure as well.
Yes, you can do the bank remittance in the program currency via wire transfer or debit card. Please contact your program advisor, or email us at learner.success@emeritus.org for details.
Yes! Please email us at learner.success@emeritus.org with the details of the program you are interested in, and we will assist you.
Please email learner.success@emeritus.org with your invoicing requirements and the specific program you’re interested in enroling in.
Yes, the flexible payment option allows a participant to pay the program fee in installments. This option is made available on the payment page and should be selected before submitting the payment.
Please email us at learner.success@emeritus.org for assistance.
You may request a full refund within seven days of your payment or 14 days after the published start date of the program, whichever comes later. If your enrollment had previously been deferred, you will not be entitled to a refund. Partial (or pro-rated) refunds are not offered. All withdrawal and refund requests should be sent to admissions@emeritus.org.
After the published start date of the program, you have until the midpoint of the program to request to defer to a future cohort of the same program. A deferral request must be submitted along with a specified reason and explanation. Cohort changes may be made only once per enrollment and are subject to availability of other cohorts scheduled at our discretion. This will not be applicable for deferrals within the refund period, and the limit of one deferral per enrollment remains. All deferral requests should be sent to admissions@emeritus.org.
Flexible payment options available.