ITU-ACM-20-21-Spring-Machine-Learning
Instructors
Mehmet Yiğit Ateş
Computer Engineering #2 @I.T.U
Prerequisities
- Basic knowledge of Python programming language.
- Basic knowledge of Calculus and Linear Algebra.
Goal
- Making attendees familiar to basic Machine Learning Topics such as Classification and Regression
- Making attendees familiar with working mechanism and math of Machine Learning Algorithms
- Introducing Artificial Neural Networks
- Introducing basics of NumPy computational library
Syllabus
| #Date | #Topic | #Description |
|---|---|---|
| 16.03.2021 | Introduction to Machine Learning | How Machine Learning Works, NumPy and Gradient Descent |
| 23.03.2021 | Linear Regression | Linear and Polynomial Regression, Bias and Variance Concepts |
| 30.03.2021 | Logistic Regression | Classification using Logistic Regression and it's implementation |
| 06.04.2021 | K Nearest Neighbor Algorithms | KNN Classification and Regression, K-Means Clustering |
| 13.04.2021 | Tree Based Algorithms | Decision Tree, Random Forest, Boosting Methods and their implementations |
| 20.04.2021 | Neural Networks | Artificial Neural Networks and it's implementation |
Lessons will be around 1:30 - 2 hours (May Change)
(Lectures will be held online using Zoom)
Important Links
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Suggested Readings
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Project
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