STARTS ON

27 March 2019

DURATION

3 Months, Online
6-8 hours per week

COURSE FEES

USD 1,200

info Flexible payment available

Why Enroll for the Applied Machine Learning course?

Machine Learning has become an entrenched part of everyday life. The books we buy, the movies we watch, the sports we follow, the driving directions we get are driven by Machine Learning algorithms. It is one of the most exciting fields of computing today. And Machine Learning practitioners are in high demand, with a shortfall of 250,000 data scientists forecast.

At Columbia Engineering, we are fascinated by the possibilities of Machine Learning. We have created the Applied Machine Learning course, in partnership with EMERITUS, to help students across the world apply Machine Learning to improve every aspect of human life.

Going beyond the theory, our approach invites participants into a conversation, where learning is facilitated by live subject matter experts and enriched by practitioners in the field of machine learning.

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Your Learning Journey

Faculty Video Lectures

Quizzes / Assignments

Q&A Sessions with Course Leaders

Moderated Discussion Boards

Application
Projects

Live Online Teaching

  • Supervised Learning
  • Module 1: Regression
    Maximum Likelihood, Least Squares, Regularization
  • Module 2: Bayesian Methods
    Bayes Rule, MAP Inference, Active Learning
  • Module 3: Foundational Classification Algorithms
    Nearest Neighbors, Perceptron, Logistic Regression
  • Module 4: Refinements to Classification
    Kernel Methods, Gaussian Process
  • Module 5: Intermediate Classification Algorithms
    SVM, Trees, Forests and Boosting
  • Unsupervised Learning
  • Module 6: Clustering Methods
    K-Means Clustering, E-M, Gaussian Mixtures
  • Module 7: Recommendation Systems
    Collaborative Filtering, Topic Modeling, PCA
  • Module 8: Sequential Data Models
    Markov and Hidden Markov Models, Kalman Filters
  • Module 9: Association Analysis
  • Module 10: Model Selection
    Model Comparisons, Analysis Considerations

PRE-REQUISITES: The course requires an undergraduate knowledge of statistics, (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation, etc.) calculus, linear algebra, and probability.

You should be comfortable with Python or any other programming language. All assignments/application projects will be done using the Python programming language using one or more of the following packages pandas, NumPy, Matplotlib, seaborn, scikit-learn, PyMC3 etc.

All assignments and application projects will be done using the Python programming language.

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Application Projects

Note: All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.

Faculty

John W. Paisley
John W. Paisley
Columbia University Associate Professor, Electrical Engineering
Affiliated Member, Data Sciences Institute

John has a PhD from Duke and has been a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley. John Paisley’s research focuses on developing models for large-scale text and image processing applications. He is particularly interested in Bayesian models and posterior inference techniques that address the big data problem.

Course Leaders

Carleton Smith
Carleton Smith
Course Leader, EMERITUS

Carleton Smith is a Data Science educator and practitioner in Chicago, IL. Carleton has taught several data science courses at General...

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Jacob Koehler
Jacob Koehler
Course Leader, EMERITUS

Jacob is a mathematics educator, with a PhD in mathematics education from Columbia University. Currently, he teaches...

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James Faghmous
James Faghmous
Course Leader, EMERITUS

James H. Faghmous is a visiting assistant professor at Stanford University where he researches and mentors...

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*Course Leaders are subject to change

Certificate of Completion

certificate

Certificate of Completion

Upon successful completion of the course, participants will receive a verified digital certificate from EMERITUS in collaboration with Columbia Engineering Executive Education.

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