EXECUTIVE EDUCATION

Machine Learning: Fundamentals and Algorithms

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Course Dates

STARTS ON

June 16, 2021

Course Duration

DURATION

10 weeks, online
5-10 hours/week

Course Duration

PROGRAM FEE

US$2,250

Course Information Flexible payment available
Note: The course requires a functional knowledge of high-school-level linear algebra, calculus, probability, statistics, and Python programming. Take a self-assessment test to verify your readiness for the math and Python content in this program.

Advance Your Skills in Machine Learning & AI

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.

#1 in Artificial Intelligence Specialty and Graduate Programs for Computer Science.

Source: U.S. News & World Report

Key Outcomes

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:

  • Synthesize components of machine learning to create functional tools for prediction of unseen data.
  • Implement and analyze learning algorithms for classification, regression and clustering.
  • Use concepts from probability, statistics, linear algebra, calculus and optimization to describe and refine the inner workings of machine learning algorithms.

Program Modules

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.

Module 1:

Decision Trees

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.

Module 2:

K-Nearest Neighbor

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.

Module 3:

Model Selection

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.

Module 4:

Linear Regression

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.

Module 5:

Optimization

In this module, you will determine how convexity affects optimization and implement linear regression with optimization by stochastic gradient descent.

Module 6:

Binary Logistic Regression

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.

Module 7:

Regularization

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.

Module 8:

Neural Networks

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.

Module 9:

Backward Propagation

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.

Module 10:

K-Means and Others Learning Paradigms

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.

Module 1:

Decision Trees

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.

Module 6:

Binary Logistic Regression

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.

Module 2:

K-Nearest Neighbor

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.

Module 7:

Regularization

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.

Module 3:

Model Selection

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.

Module 8:

Neural Networks

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.

Module 4:

Linear Regression

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.

Module 9:

Backward Propagation

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.

Module 5:

Optimization

In this module, you will determine how convexity affects optimization and implement linear regression with optimization by stochastic gradient descent.

Module 10:

K-Means and Others Learning Paradigms

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.

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Program Experience

Python Coding Exercise in Each Module

Bite-Sized Learning

Knowledge Checks

Dedicated Program Support Team

Mobile Learning App

Peer Discussion

Who Should Attend

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:

  • Software Engineer
  • Software Developer
  • Automation Engineer
  • Design Engineer

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:

  • Analyst
  • Business Analyst
  • Data Scientist
  • Data Analyst

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:

  • Tech Lead
  • Senior Engineer
  • Senior Developer
  • VP Engineering
  • VP Technology
  • VP Analytics
  • Director of Business Systems & Information Technology
  • Director of Customer Experience
  • Data & Integration Director
  • Technology Director

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.

Program Faculty

Patrick Virtue

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... More info

Matt Gormley

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 ... More info

Certificate

Certificate

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.

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Your 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.

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