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CSC-272 Data Mining (Fall 2024)

Calendar
Syllabus
Course Project
- Data sets and Project ideas

Introduction

Programming for Data Science

1. Vectorized Operations on Data
2. Encoding and Representation
3. Collecting Data
- 3.1. APIs
- 3.2. Web Scraping
4. Visualizing Data

Mathematics for Data Science

5. Prob, Stats and Logs
6. Linear Algebra and Geometry
7. Calculus
- 7.1. Integral Calculus
- 7.2. Differential Calculus

Problems in Data Science

8. Supervised Learning
- 8.6. Classification
- 8.7. Regression
9. Unsupervised Learning
- 9.1. Clustering
10. Reinforcement Learning

Models in Data Science

11. Graphical Models
- 11.1. Naive Bayes
12. Generalized Linear Model
13. Neural Networks

Algorithms in Data Science

14. Expectation Maximization
15. Gradient Descent
16. Backpropogation

Problems with Data Science

17. Representation Matters
18. Positive Feedback Loops
19. Decision Boundaries
20. Majority Rule

Applications of Data Science

21. Recommendation Engine

Colab

.ipynb

Expectation Maximization

14. Expectation Maximization#

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13. Neural Networks

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15. Gradient Descent

By Syed Fahad Sultan

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