CSE 144: Applied ML: Deep Learn
University of California, Santa Cruz
Provides a practical and project-oriented introduction to deep learning techniques. Starts with a review of basic elements of machine learning: training and testing, loss function, gradient descent, linear regression, and logistic regression. Moves on to common deep learning models: feedforward networks, convolutional networks for image recognition, recurrent networks and LSTM for temporal and sequential data, attention models and transformers. Some practical concepts for deep learning, including how to find model parameters, how to train large scale models, techniques for regularization and avoid overfitting, are also covered. A very basic introduction to more complex techniques such as deep reinforcement learning, neural symbolic models and diffusion models is provided in week 9. Selected student teams present their course projects in the last week. (Formerly offered as Applied Machine Learning.)
Average GPA: 3.49
Grade distribution records: 1,277 students across 15 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 110 | 8.6% |
| A | 407 | 31.9% |
| A- | 233 | 18.2% |
| B+ | 120 | 9.4% |
| B | 136 | 10.6% |
| B- | 66 | 5.2% |
| C+ | 38 | 3.0% |
| C | 20 | 1.6% |
| C- | 8 | 0.6% |
| D+ | 2 | 0.2% |
| D | 9 | 0.7% |
| D- | 1 | 0.1% |
| F | 16 | 1.3% |
| P | 54 | 4.2% |
| NP | 18 | 1.4% |
| S | 5 | 0.4% |
| U | 1 | 0.1% |
| W | 33 | 2.6% |
Based on 1,277 student grade records across 15 terms and 4 professors.
Instructors
- Narges Norouzi 567 students, Average GPA 3.33
- Cihang Xie 336 students, Average GPA 3.69
- Yi Zhang 236 students, Average GPA 3.46
- Yuyin Zhou 138 students, Average GPA 3.63