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+# Course Overview
+
+This course covers the fundamentals of machine learning, including supervised and unsupervised learning, neural networks, and reinforcement learning. The course is divided into 12 lectures, each lasting 90 minutes.
+
+## Assignment 1
+
+The first assignment is due on **August 31, 2026**. It requires you to implement a simple linear regression model and evaluate its performance.
+
+## Lecture Schedule
+
+| Lecture | Topic |
+|---------|-------|
+| 1 | Introduction to ML |
+| 2 | Data Preprocessing |
+| 3 | Linear Regression |
+| 4 | Logistic Regression |
+| 5 | Decision Trees |
+| 6 | Random Forests |
+| 7 | Support Vector Machines |
+| 8 | Neural Networks |
+| 9 | Convolutional Neural Networks |
+| 10 | Recurrent Neural Networks |
+| 11 | Reinforcement Learning |
+| 12 | Project Presentations |