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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 7681 | yes | Columbia 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
Instructors
Articles
1 Sep'23, 12:44 PM
1 Sep'23, 12:27 PM
1 Sep'23, 12:02 PM
1 Sep'23, 11:26 AM
1 Sep'23, 09:57 AM
1 Sep'23, 08:48 AM
1 Sep'23, 07:11 AM
31 Aug'23, 02:07 PM
31 Aug'23, 12:10 PM
26 Aug'23, 11:14 AM
21 Aug'23, 07:18 AM
18 Aug'23, 11:51 AM
6 Jul'23, 07:27 AM
21 Jun'23, 12:18 PM
13 Jun'23, 09:54 AM
4 Apr'23, 11:39 AM
27 Mar'23, 07:53 AM
22 Mar'23, 12:57 PM
22 Mar'23, 11:00 AM
1 Sep'23, 12:44 PM
1 Sep'23, 12:27 PM
1 Sep'23, 12:02 PM
1 Sep'23, 11:26 AM
1 Sep'23, 09:57 AM
1 Sep'23, 08:48 AM
1 Sep'23, 07:11 AM
31 Aug'23, 02:07 PM
31 Aug'23, 12:10 PM
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