UCI-MATH 10: Introduction to Programming for Data Science

Math 10 serves as the introductory course in programming and machine learning algorithms, mainly targeted for the students with mathematical background and have interests in the Data Science specialization. In addition to the introduction of popular python data science packages (Numpy, Matplotlib, Pandas, Seaborn and Scikit-learn), this course also emphasizes the understandings of rationales underlying the programming language and machine learning algorithms.

(Updated Winter 2021)

Lecture Notes

Lecture	Contents
1	Introduction and Walkthrough I: Data Science and Machine Learning
2	Introduction and Walkthrough II: Programming Language
3	Python Basic I: Expressions, Variables and Objects
4	Python Basic II: Object- Mutable/Immutable, Attributes/Methods
5	Python Basic III: Control Flows and Functions
6	Python Basic IV: Class and Modules
7	Data Science Basic I: Introduction to Numpy
8	Data Science Basic II: Matplotlib and Image Processing
9	Data Science Basic III: Introduction to Pandas
10	Machine Learning I: Overview of Machine Learning and Supervised Learning -- Linear Regression
11	Machine Learning II: Supervised Learning -- Classification: Logistic Regression
12	Machine Learning III: Supervised Learning -- Classification: k-NN, Decision Tree and Random Forest
13	Machine Learning IV: Unsupervised Learning -- Dimension Reduction: Principal Component Analysis
14	Machine Learning V: Unsupervised Learning -- Dimension Reduction: Manifold Learning
15	Machine Learning VI: Unsupervised Learning --Clustering: K-Means
16	Machine Learning VII: Neural Network and Introduction to Deep Learning

Textbooks and References

There will be NO official textbook for this course. You may find the following references helpful:

• For Basic Python Programming : A Byte of Python

• For Machine Learning Codes in Python: Python Data Science Handbook

• For Machine Learning Applications and Theories: An Introduction to Statistical Learning; The Elements of Statistical Learning

  latest update:Probabilistic Machine Learning: An Introduction (2021 edition) by Dr. Kevin P. Murphy

• For Deep Learning:Deep Learning

Please also check the links in our lecture notes.

(Optional) Advanced Materials:

For students who are already familiar with the basic course materials and aim to get A+ for this course, I suggest you to learn the following packages by yourself, discuss with me in the office hours and try to apply them in the final projects.

• Accelerating Python/Numpy with translation into optimized machine codes: Numba
• Parallel Computing: Dask
• GPU realizations of data science/machine learning packages based on cuda: cuDF and cuML
• Deep Learning Packages:Tensorflow and PyTorch