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.


  • 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


  • Homework (50%)
    • Six programming assignments
  • Course Project (30%)
    • Students do course projects solo
    • Online poster sessions will be hosted
  • Final Exam (20%)
  • Bonus


  • Attendance is encouraged but not mandatory

Class Schedule

Sep 1Introduction
Sep 3Introduction
Sep 8Linear Algebra Recap
Sep 10Linear Regression
Sep 15Linear Regression
Sep 17Linear Regression
Sep 22Bias and Variance
Sep 24Perceptron
Sep 29Logistic Regression
Oct 1Multiclass Classification
Oct 6Multilayer Neural Network
Oct 8Multilayer Neural Network
Oct 13Deep Learning Packages
Oct 15Convolutional Neural Network-I
Oct 20Convolutional Neural Network-II
Oct 22Bag of Tricks-I
Oct 27Bag of Tricks-II
Oct 29Autoencoder
Nov 3Text Data Processing Basis
Nov 5Word Embeddings
Nov 10Recurrent Neural Networks-I
Nov 12Recurrent Neural Networks-II
Nov 17Neural Machine Translation
Nov 19Transformer
Nov 24Transformer
Dec 1Poster Session
Dec 3Poster Session
Dec 8Project Presentation
Dec 10Final Review