Course Timetable

“Computational thinking is the thought processes involved in formulating problems and their solutions so that the solutions are in a form that can be effectively carried out by an information-processing agent” [Cuny, Snyder, Wing 10]. Computational thinking and its outcomes (i.e., computers, software, and their usage) are increasingly shaping the world in which we live. In order to be productive citizens of the 21st century, UBC students need to have the opportunity to learn concepts such as how data can be processed to gain insights, how computers use their personal data, and why computational thinking enables some amazing tasks (e.g., finding directions, sharing videos, and communicating instantly) but is as yet so bad at others (e.g., translating documents between languages). CPSC 100 will give non-computer science majors key insights into (1) the building blocks necessary for computational thinking (2) applications of computational thinking and (3) how computational thinking and its applications impact the world around them. Students will explore the past, present, and future of computing including a student directed exploration of computing and computational thinking issues in the news. This course is targeted to first year students, but is open to all.

When you have completed this course, you will be able to systematically design small programs using the Python programming language. You will be able to take a problem from an academic discipline of your choice, appropriately represent the domain information as data in your program, and design functions that successfully solve your problem. You will learn how to break down a large problem into well-structured sub-problems, each of which will be solved systematically. You will understand how programs work which will help you better understand the computer-based world that you interact with every day. You will be better able to communicate with computer scientists in your future workplace. This course is designed for students who are not computer science majors. There are no prerequisites.

When you have completed this course, you will be able to systematically design small programs using the Python programming language. You will be able to take a problem from an academic discipline of your choice, appropriately represent the domain information as data in your program, and design functions that successfully solve your problem. You will learn how to break down a large problem into well-structured sub-problems, each of which will be solved systematically. You will understand how programs work which will help you better understand the computer-based world that you interact with every day. You will be better able to communicate with computer scientists in your future workplace. This course is designed for students who are not computer science majors. There are no prerequisites.

CPSC 121 explores formal modeling systems that help us to understand and to explore the capabilities of computers and, more generally, of any problem solving process. Our exploration of these systems will be guided by the desire to answer the following four practical questions:

1. How can we convince ourselves that an algorithm does what it's supposed to do?
2. How do we determine whether or not one algorithm is better than another one?
3. How does the computer (e.g. Dr. Racket) decide if the characters of your program represent a name, a number, or something else? How does it figure out if you have mismatched " " or ( )?
4. As of 2019, processors have six to seven billion transistors. How can we build a computer that is able to execute a user-defined program?

CPSC 121 explores formal modeling systems that help us to understand and to explore the capabilities of computers and, more generally, of any problem solving process. Our exploration of these systems will be guided by the desire to answer the following four practical questions:

1. How can we convince ourselves that an algorithm does what it's supposed to do?
2. How do we determine whether or not one algorithm is better than another one?
3. How does the computer (e.g. Dr. Racket) decide if the characters of your program represent a name, a number, or something else? How does it figure out if you have mismatched " " or ( )?
4. As of 2019, processors have six to seven billion transistors. How can we build a computer that is able to execute a user-defined program?

The following broad topics will be covered. The main learning goals for each topic are also provided.

Data Abstraction

• describe how data abstraction makes possible the construction of larger software systems
• specify, use, test and implement a data abstraction in Java

Control Flow Models

• produce inter- and intra-method control flow diagrams from Java code

Type Hierarchies, Polymorphism and Dispatching

• trace the execution of Java source code that uses polymorphism, inheritance and dynamic dispatching
• determine if one type is substitutable for another

Robust Data Abstractions

• specify, test and implement a robust data abstraction using exceptions

Object-Oriented Design

• record the design of an existing system using a UML class diagram
• extract UML sequence diagrams from existing code
• express a UML class diagram as a Java program
• make effective use of the Java Collections Framework

Design Patterns

• apply the Composite, Observer, and Iterator patterns to a given design problem

The preliminary syllabus is here: https://www.students.cs.ubc.ca/~cs-304/2021W1/cpsc304_2021w1_syllabus.pdf 

This course covers application of machine learning tools, with an emphasis on solving practical problems. Also included in the course are data cleaning, feature extraction, supervised and unsupervised machine learning, reproducible workflows, and communicating results.