# CS 5665: Introduction to Data Science

Fall 2022, *3:00 pm to 4:15 pm on TR in Old Main 326*

## 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.

## 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, Luca Antiga, Thomas Viehmann. (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%)
- Five programming assignments

- Course Project (30%)
- Students do course projects as groups
- Online poster sessions will be hosted

- Final Exam (20%)

## Attendance

- Attendance is not mandatory but encouraged

## Schedule

Date | Topic |
---|---|

Aug 30 | Introduction |

Sep 1 | Introduction |

Sep 6 | Supervised Learning |

Sep 8 | Linear Regression |

Sep 13 | Linear Regression |

Sep 15 | Linear Regression |

Sep 20 | Linear Regression |

Sep 22 | Bias and Variance |

Sep 27 | Regularization |

Sep 29 | Perceptron |

Oct 4 | Logistic Regression |

Oct 6 | Logistic Regression |

Oct 11 | Multiclass Classification |

Oct 13 | Multilayer Neural Network |

Oct 18 | Multilayer Neural Network |

Oct 20 | Deep Learning Package |

Oct 25 | Convolutional Neural Network-I |

Oct 27 | Convolutional Neural Network-II |

Nov 1 | Bag of Tricks-I |

Nov 3 | Bag of Tricks-II |

Nov 8 | Auto-encoder |

Nov 10 | Text Data Processing and Word Embeddings |

Nov 15 | Recurrent Neural Networks-I |

Nov 17 | Recurrent Neural Networks-II |

Nov 22 | Canceled |

Nov 24 | Thanksgiving |

Nov 29 | Project Presentation |

Dec 1 | Project Presentation |

Dec 6 | Final Review |

Dec 8 | Q&A |