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Week 1: Algorithms 1

  1. Introduction to Algorithms and Machine Learning
  2. Introduction to Algorithms
  3. Tools to Analyze Algorithms
  4. Algorithmic Technique: Divide and Conquer
  5. Divide and Conquer Example: Investing
  6. Randomization in Algorithms
  7. Application Area Scheduling 1
  8. Application Area Scheduling 2

Week 2: Algorithms 2

  1. Graphs
  2. Some Ideas Behind Map Searches 1
  3. Some Ideas Behind Map Searches 2
  4. Application of Algorithms: Stable Marriages Example
  5. Dictionaries and Hashing
  6. Search Trees
  7. Dynamic Programming 1
  8. Dynamic Programming 2

Week 3: Algorithms 3 and Application to Personal Genomics

  1. Linear Programming 1
  2. Linear Programming 2
  3. NP-completeness 1
  4. NP-completeness 2
  5. NP-completeness 3 and Summary
  6. Introduction to Personal Genomics
  7. Massive Raw Data In Genomics
  8. Data Science On Personal Genomes
  9. Interconnectedness Of Personal Genomes
  10. Personal Genomics Case Studies
  11. Personal Genomics Conclusion

Week 4: Machine Learning

  1. Algorithms in Machine Learning
  2. What Is Machine Learning 1
  3. What Is Machine Learning 2
  4. Classification
  5. Linear Classifiers
  6. Ensemble Classifiers
  7. Model Selection
  8. Cross Validation
  9. Machine Learning Summary

Week 5: Machine Learning Applications

  1. Machine Learning Application: Introduction to Probabilistic Topic Models
  2. Probabilistic Modeling 1
  3. Probabilistic Modeling 2
  4. Topic Modeling
  5. Probabilistic Inference
  6. Machine Learning Application: Prediction of Preterm Birth
  7. Data Description and Preparation
  8. Methods for Prediction of Preterm Birth
  9. Results and Discussion
  10. Summary and Conclusion
  11. Relation Between Machine Learning and Statistics

Grading Policy

Assignments count towards 100% of the grade. There will be no midterm exam and no final exam.

Two lowest assignments will be dropped.

Students have 2 attempts to complete assignments, unless otherwise noted in the assignment description.

Passing grade for the course is 60% or higher.

Certificate Information

This course is part of a three-part Data Science and Analytics in Context XSeries from ColumbiaX. If you earn a passing grade in all three courses in this series for a verified certificate, you will also receive an XSeries certificate for the series.

For self-paced courses, certificates are available on-demand, which means that once a student passes the course, they can request their certificate through their Progress page. 
Request and Download a Certificate

To receive your certificate at any time after you qualify, follow these steps.

  1. On the course Progress page, select Request Certificate.

    After you request your certificate, the certificate creation process may take up to 48 hours. When your certificate is ready, a “Your certificate is available” message appears on your Progress page.

  2. Download your certificate from the Progress page or from your dashboard.

    • To download your certificate from the Progress page, select Download Your Certificate in the upper right corner of the page.
    • To download your certificate from your dashboard, select Download Your Certificate next to the name of your course.