Machine Learning and Deep Learning Syllabus
Machine Learning and Deep Learning Syllabus⌗
Topics⌗
| Fundamentals | Neural Networks | Advanced |
|---|---|---|
| Linear Regression | Intro to Neural Networks | Unsupervised Learning |
| Model Validation | Neural Networks in Practice | Transformers |
| Logistic Regression | CNNs | Model Evaluation |
Evaluation Criteria⌗
| Component | Weight |
|---|---|
| Midterm Exam | 50% |
| Project Final | 50% |
Recommended Resources⌗
Main Textbooks:⌗
| Title | Author | Link |
|---|---|---|
| Deep Learning with Python | François Chollet | Notebooks |
| Understanding Deep Learning | Simon J.D. Prince | Website |
| The Hundred-Page Machine Learning Book | Andriy Burkov | Website |
Relevant Survey Paper:⌗
| Title | Author | Link |
|---|---|---|
| Deep Learning in Neural Networks: An Overview | Jürgen Schmidhuber |