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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Courses and Certificate Fees

Fees InformationsCertificate AvailabilityCertificate Providing Authority
INR 7681yesColumbia University, New York

The Syllabus

  • Course Introduction
  • Course Overview
  • Introduce yourself

  • What algorithms have you heard before?
  • Week 1 Reading List
  • 1.1 Algorithms and Machine Learning
  • 1.2 Introduction to Algorithms
  • 1.2 Assignment
  • 1.3 Tools to Analyze Algorithms
  • 1.3 Assignment
  • 1.4 Algorithmic Technique: Divide and Conquer
  • 1.5 Divide and Conquer Example: Investing
  • 1.5 Assignment
  • 1.6 Randomization in Algorithms
  • 1.6 Assignment
  • 1.7 Application Area Scheduling 1
  • 1.8 Application Area Scheduling 2
  • 1.8 Optional Assignment

  • Week 2 Reading List
  • 2.1 Graphs
  • 2.1 Assignment
  • 2.2 Some Ideas Behind Map Searches 1
  • 2.3 Some Ideas Behind Map Searches 2
  • 2.3 Assignment
  • 2.4 Application of Algorithms: Stable Marriages Example
  • 2.4 Assignment
  • 2.5 Dictionaries and Hashing
  • 2.5 Assignment
  • 2.6 Search Trees
  • 2.6 Assignment
  • 2.7 Dynamic Programming 1
  • 2.8 Dynamic Programming 2
  • 2.8 Assignment

  • 3.1 Linear Programming 1
  • 3.2 Linear Programming 2
  • 3.2 Assignment
  • 3.3 NP-completeness 1
  • 3.4 NP-completeness 2
  • 3.5 NP-completeness 3 and Summary
  • 3.5 Assignment
  • 3.6 Introduction to Personal Genomics
  • 3.6 Assignment
  • 3.7 Massive Raw Data In Genomics
  • 3.7 Assignment
  • 3.8 Data Science On Personal Genomes
  • 3.9 Interconnectedness Of Personal Genomes
  • 3.9 Assignment
  • 3.10 Personal Genomics Case Studies
  • 3.10 Assignment
  • 3.11 Personal Genomics Conclusion
  • Which topic of this week do you like best?

  • Week 4 Reading List
  • 4.1 Algorithms in Machine Learning Introduction
  • 4.2 What Is Machine Learning 1
  • 4.3 What Is Machine Learning 2
  • 4.4 Classification
  • 4.4 Assignment
  • 4.5 Linear Classifiers
  • 4.5 Assignment
  • 4.6 Ensemble Classifiers
  • Survey about classifiers
  • 4.6 Assignment
  • 4.7 Model Selection
  • 4.8 Cross Validation
  • 4.8 Assignment
  • 4.9 Machine Learning Summary
  • 4.10 (optional) Assignment

  • Week 5 Reading List
  • 5.1 Machine Learning Application: Introduction to Probabilistic Topic Models
  • 5.2 Probabilistic Modeling 1
  • 5.3 Probabilistic Modeling 2
  • 5.4 Topic Modeling
  • 5.5 Probabilistic Inference
  • 5.5 Assignment
  • 5.6 Machine Learning Application: Prediction of Preterm Birth
  • 5.7 Data Description and Preparation
  • 5.7 Assignment
  • 5.8 Methods for Prediction of Preterm Birth
  • 5.9 Results and Discussion
  • 5.10 Summary and Conclusion
  • 5.10 Assignment
  • 5.11 Assignment
  • 5.11 Relation Between Machine Learning and Statistics
  • 5.12 Topic Model Assignment
  • 5.13 Course Conclusion
  • Where can we apply machine learning?

  • Overview of AWS Cloud Service

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