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scikit-image

scikit-learn

pandas (software)

Jupyter Notebook

Code Complexity

Memory management

Python

List (abstract data type)

File Processing

pandas DataFrame

Data Science Libraries

Joins

Images

Statistics

Indexes

Control Structures

Data structure

Data Visualization

Class (computer programming)

Algorithmic Efficiency

Ethics

p-hacking

Tree (data structure)

NumPy

String (computer science)

Computer file

Machine learning

Object (computer science)

Geospatial Data

Convolution

Algorithmic fairness

Algorithmic Privacy

CSE 163 Intermediate Data Programming University of Washington Summer 2022 This course offers an intermediate level of data programming, focusing on different data types, data science tools, code complexity, and memory management. It emphasizes the efficient use of concepts for data programming. The world has become data-driven. Domain scientists and industry increasingly rely on data analysis to drive innovation and discovery; this reliance on data is not only restricted to science or business, but also is crucial to those in government, public policy, and those wanting to be informed citizens. As the size of data continues to grow, everyone will need to use powerful tools to work with that data.

This course teaches intermediate data programming. It is a follow on to CSE142 (Computer programming I) or CSE160 (Data Programming).

The course complements CSE143, which focuses more deeply on fundamental programming concepts and the internals of data structures. In contrast, CSE163 emphasizes the efficient use of those concepts for data programming.

In this course, students will learn:

1. More advanced programming concepts than in CSE142 or CSE160 including how to write bigger programs with multiple classes and modules.
1. How to work with different types of data: tabular, text, images, geo-spatial.
1. Ecosystem of data science tools including Jupyter Notebook and various data science libraries including scikit image, scikit learn, and Pandas data frames.
1. Basic concepts related to code complexity, efficiency of different types of data structures, and memory management.  This is class is designed as the second introductory programming course that focuses on writing programs that work with data. The prerequisites for the class require students having taken CSE 142 or CSE 160 and the class has been designed to be accessible to students from either of those backgrounds. Students that have taken 143 are welcome to take this class as it will serve as a complement to the material learned in 143 with only minor overlap.

Because this course will have students coming from many different class backgrounds, the first couple of weeks will be pretty different for students depending on what classes they have taken. Here is what we expect students to see in the first weeks based on their background:

- 142: The first two weeks might go pretty fast, but will be doable since you already know all the concepts (loops, conditionals, methods) and you are just learning all the new “words” in Python to use those concepts. This might require a little bit of extra practice early in the quarter so you are familiar with translating all the ideas you have learned in 142 to this new language. The first week has been designed to be a recap of all things 142 so you don’t also have to be learning a ton of new material while learning a new language in the first week.
 - 160: The first week will just be a bit of a review for you, but the class will start covering material you haven’t seen before starting in the second week.
- 143: You are in a similar boat as the 142 students, where you know a lot of the concepts but don’t know the Python language. You’ll probably see a few things that you saw in 143 in this class, but I think the new context of processing data in a new language will still keep it new, exciting, and challenging. 

CSE 163 Intermediate Data Programming

Computer Programming

Data Science

Intelligent agent

Game theory

Adversarial Search

Knowledge representation and reasoning

Logic

Uncertain Knowledge

Bayes' theorem

Bayesian network

Hidden Markov Model (HMM)

Robot Motion Planning

Classical Planning

Probabilistic Planning

Reinforcement learning (RL)

Supervised learning

Unsupervised learning

Natural Language Processing (NLP)

Robotics

Computer Vision

Philosophy of AI

Social and Ethical Issues in AI

Ethical Dilemmas in AI

CS1410 Artificial Intelligence Brown University Fall 2022 CS1410 at Brown University delves into the realm of Artificial Intelligence. Using the 3rd edition of "Artificial Intelligence, A Modern Approach" by Russell & Norvig, students explore intelligent agents, game theory, knowledge representation, logic, probabilistic learning, NLP, robotics, computer vision, and ethical implications of AI.   Prerequisites to CS1410 include (CSCI 0160, 0180, 0190, or 0200) and (CSCI 0220, 1450, 0450, APMA 1650 or 1655) or minimum score of WAIVE in 'Graduate Student PreReq'. ### Required Text

*Artificial Intelligence, A Modern Approach* Russell & Norvig, **3rd** Edition.

### On Lectures

The textbook contains everything you need to know.

Lectures contain everything you need to know.

Lecture notes **do not contain everything you need to
know**.

CS1410 Artificial Intelligence

Artificial Intelligence

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2 courses cover this concept

CSE 163 Intermediate Data Programming

CS1410 Artificial Intelligence