Applied Machine Learning

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.

Duration and Course Fee

  • Starts 20 Nov 2018
  • 3 Months
  • 6 – 8 hours per week
  • Course Fees USD 1200
 

Faculty

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

Columbia Engineering

Course Highlights

  • Faculty Video Lectures
  • Peer Learning
    Moderated Discussion Boards
  • assignment icon
    Quizzes / Assignments
  • Real World Applications
    Application Projects
  • Q&A Sessions with Course Leaders
  • webinar
    Live Online Teaching

SYllabus

Supervised Learning

Maximum Likelihood, Least Squares, Regularization

Bayes Rule, MAP Inference, Active Learning

Nearest Neighbors, Perceptron, Logistic Regression

Kernel Methods, Gaussian Process

SVM, Trees, Forests and Boosting

Unsupervised Learning

K-Means Clustering, E-M, Gaussian Mixtures

Collaborative Filtering, Topic Modeling, PCA

Markov and Hidden Markov Models, Kalman Filters

Model Comparisons, Analysis Considerations

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

Application Projects

Movie Recommendation Engine

Movie Recommendation Engine

You will build a movie recommendation engine by applying collaborative filtering and topic modelling techniques. You use a dataset which contains 20 million viewer ratings of 27,000 movies.

House Price Prediction

House Price Prediction

You will write code to predict house prices based on several parameters available in the Ames City dataset compiled by Dean De Cock using least squares linear regression and Bayesian linear regression.

Human Activity Prediction

Human Activity Prediction

You will predict the human activity (walking, sitting, standing) that corresponds to the accelerometer and gyroscope measurements by applying the nearest neighbours technique.

Credit Card Fraud Detection

Credit Card Fraud Detection

You will detect potential frauds using credit card transaction data. You will apply the random forest method to identify fraudulent transactions.

Marketing Segmentation

Marketing Segmentation

You will create market segments using the US Census dataset and by applying the k-means clustering method.

BENEFITS TO THE LEARNER

Intellectual Capital

Intellectual Capital

  • Global Education
  • Rigorous and experiential curriculum
  • World-renowned faculty
  • Globally Connected Classroom: Peer to Peer Learning Circles
  • Action Learning: Learning by Doing

Brand-Capital

Brand Capital

  • Certificate from EMERITUS in collaboration with Columbia
    Engineering Executive Education

Social-Capital

Social Capital

  • Build new networks through peer interaction
  • Benefit from diverse class profiles

Career-Capital

Career Capital

  • Professional Acceleration through our enriched leadership toolkit
  • Learn while you earn
  • Get noticed. Get ahead.

Duration and Course Fee

  • Starts November 20, 2018
  • 3 Months
  • 6 – 8 hours per week
  • Course Fees USD 1200

Faculty

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

Good explanation of content and videos by Professor Rogers.

Marcel Gonska CEO & Founder, WLC GmbH
GERMANY

Duration and Course Fee

  • Starts 20 Nov 2018
  • 3 Months
  • 6 – 8 hours per week
  • Course Fees USD 1200
 

Faculty

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

Columbia Engineering

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