Machine Learning: Fundamentals and Algorithms


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    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.


    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 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:


    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:


    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.


    Python Coding Exercise in Each Module

    Bite-Sized Learning

    Knowledge Checks

    Dedicated Program Support Team

    Mobile Learning App

    Peer Discussion

    Program Faculty


    Upon successful completion of the program, participants will be awarded a verified digital diploma by Emeritus Institute of Management, in collaboration with MIT Sloan, Columbia Business School Executive Education & Tuck School of Business at Dartmouth.