CS 5665: Introduction to Data Science
Fall 2020, 1:30 pm to 2:45 pm on TR via WebBroadcast
Course Descriptions
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data [link]. In recent years, deep learning approaches have obtained very high performance on various data analysis tasks. This course focuses on introducing the basic deep learning approaches. The goal of this course is for students to learn how to use deep learning for solving real-world data analysis problems, especially in the fields of computer vision and natural language processing.
Topics include: Linear Regression, Logistic Regression, Feed-forward Neural Network, Convolutional Neural Network, Recurrent Neural Network, Transformers, Generative Adversarial Networks.
Prerequisites
- A solid Python programming skill
- All class assignments will be in Python.
- Basic probability and statistics
- Understand basics of probabilities, gaussian distributions, mean, standard deviation, etc.
- Basic calculus, linear algebra
- Be comfortable taking derivatives and understanding matrix/vector notation and operations. (e.g., matrix multiplication).
Course Material
- Aston Zhang, Zachary C. Lipton, Mu Li and Alexander J. Smola. (2020). Dive into Deep Learning. Available Online.
- Ian Goodfellow, Yoshua Bengio and Aaron Courville. (2016). Deep Learning. Available Online.
The following textbooks/websites are useful as additional reference:
- Eli Stevens and Luca Antiga. (2020). Deep Learning with PyTorch. Available Online.
- Michael Nielsen. Neural Networks and Deep Learning. Available Online.
- Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. (2020). Mathematics for machine learning. Available Online.
- Chapters 5, 6 7 are useful to understand vector calculus and continuous optimization
Grading
- Homework (50%)
- Six programming assignments
- Course Project (30%)
- Students do course projects solo
- Online poster sessions will be hosted
- Final Exam (20%)
- Bonus
Attendance
- Attendance is encouraged but not mandatory
Class Schedule
| Date | Topic |
|---|---|
| Sep 1 | Introduction |
| Sep 3 | Introduction |
| Sep 8 | Linear Algebra Recap |
| Sep 10 | Linear Regression |
| Sep 15 | Linear Regression |
| Sep 17 | Linear Regression |
| Sep 22 | Bias and Variance |
| Sep 24 | Perceptron |
| Sep 29 | Logistic Regression |
| Oct 1 | Multiclass Classification |
| Oct 6 | Multilayer Neural Network |
| Oct 8 | Multilayer Neural Network |
| Oct 13 | Deep Learning Packages |
| Oct 15 | Convolutional Neural Network-I |
| Oct 20 | Convolutional Neural Network-II |
| Oct 22 | Bag of Tricks-I |
| Oct 27 | Bag of Tricks-II |
| Oct 29 | Autoencoder |
| Nov 3 | Text Data Processing Basis |
| Nov 5 | Word Embeddings |
| Nov 10 | Recurrent Neural Networks-I |
| Nov 12 | Recurrent Neural Networks-II |
| Nov 17 | Neural Machine Translation |
| Nov 19 | Transformer |
| Nov 24 | Transformer |
| Dec 1 | Poster Session |
| Dec 3 | Poster Session |
| Dec 8 | Project Presentation |
| Dec 10 | Final Review |