Lower Division

Summary of Course Content:

1. Preliminaries
   1. Course description
   2. How to read and write as an ethicist in computer science
2. A primer on normative ethics
   1. The virtue of Aristotle’s ethics
   2. Pleasures and pains: Utilitarianism.
   3. Kant’s categorical imperatives: deontology.
3. What is technology?
   1. A primer on epistemology.
   2. Technological inventions that have changed the world.
   3. Technological traps.
4. Surveillance and privacy
   1. The right to privacy
   2. Informed, and uninformed consent
   3. Censorship and freedom of speech
5. Machine learning, AI, and society
   1. Algorithmic justice
   2. Clarifying AI alignment: can we encode human values?
   3. The counterfeit human.
6. Technologies and the environment
   1. Human’s relation to nature
   2. Do technologies embed environmental values?
   3. Ecological disasters and their management. 

Illustrative Reading:
A reader assembled for the class that includes work from: Neil Postman, Michael Sandel, Peter Singer, Deborah Johnson, Phil Rogaway, Timnit Gebru, James Boyle, Daniel Quinn, EM Forster, Ursula Le Guin, the ACM. 

Potential Course Overlap:
The course overlaps with ECS 188 (Ethics in an Age of Technology); however, this course is designed to be an introduction to ethics in computing. The course overlaps with ENG 190 (Professional Responsibilities of Engineers) in that it also covers ethical issues; however, ENG 190 does not primarily focus on computing, and ENG 190 also covers contracts, patents and law. The course has overlap with STS 101 (Data & Society) in its coverage of impacts of biases in data but does not cover topics like reading, exploring, manipulating, or visualizing data sets as is done in STS 101. There is overlap with ENG 001 (Introduction to Engineering) but ENG 001 covers all of engineering, not just Computer Science and Engineering. The course overlaps with PHI 024 (Introduction to Ethics) in its coverage of moral theories such as Utilitarianism, Rights-Based Ethics, etc., but PHI 024 covers the material in far greater depth and without singular focus on a computing context. 

Summary of Course Content:

• Introduction to AI
  • What is AI?
  • The power of AI and how is AI is transforming our everyday life
  • AI and Creativity
  • Emerging applications of AI in Natural Language Processing, Healthcare, CyberSecurity, Business, Self-Driving Vehicles
• A Brief History of AI
  • Birth of AI (1940-1950)
  • Rule-Based Systems (1960)
  • Rise of Machine Learning
  • Deep Learning Revolution (2010 – now)
• AI Paradigms
  • Symbolic Systems
  • Connectionism
  • Embodied AI
  • Search and Heuristics
  • Task-based vs. Generalization-based AI
• Generative AI systems
  • ChatGPT
  • DALL-E (or another image model)
  • CodeLlama
  • Babble
  • Generative Adversarial Network
  • MuseNet
  • Demos of cutting-edge AI systems - what they are good at and some common failure modes
  • Intended to give students tools that will be useful now and in the near term
• AI and Data Science
  • AI needs data
  • Understanding Data and Knowledge Discovery in the context of AI and Data Science
  • Different types of data (Structured, Unstructured, Semi-structured)
  • Example of simple data analysis
• Public Policy Integration
  • Guest speakers from policy-making backgrounds can be invited to provide insights into how AI findings in natural sciences shape regulations and guidelines.
• AI Concepts and Terminology
  • AI’s Learning Process
  • Cognitive Computing, Terminology and Related Concepts
  • Traditional Machine Learning Algorithms
  • Evaluation Criteria
  • Reinforcement Learning
  • Neural Networks
  • Deep Learning
  • Transfer Learning
  • Generative Models
  • Adversarial Models
• AI in Practice
  • Using Black Box Models in Modern AI
  • Understanding Black-Box ML Models
  • Understanding Data to Train and Test Models
  • An Example of Training a Prediction Model
  • Supervised Learning
  • Unsupervised Learning
  • Evaluating  Models, Cost Functions and Metrics
• AI Benefits
  • Safer technologies
  • More desirable work
  • Boosted creativity and personal agency
  • Accelerated scientific development
• AI Risks and Ethical Considerations
  • The importance of data in AI.
  • Examples of biases and ethical issues in modern AI
  • AI and Data Privacy
  • Risks of strong artificial intelligence
  • AI Ethics and Regulations
• The Future with AI
  • The Evolution and Future of AI
  • What will our Society look like when AI is everywhere?
  • The AI Ladder - The Journey for adopting AI successfully
  • Artificial General Intelligence (AGI)

Illustrative Reading:

• Brian Christian, “The Alignment Problem: Machine Learning and Human Values,” W. W. Norton & Company; 1st edition (October 6, 2020), ISBN 978-0393635829
• Melanie Mitchell, “Artificial Intelligence: A Guide for Thinking Humans,” Farrar, Straus and Giroux; First Edition (October 15, 2019), ISBN 978-0374257835
• Stuart Russel, “Human Compatible: Artificial Intelligence and the Problem of Control,” Viking (October 8, 2019), ISBN 978-0525558613
• Max Tegmark, “Life 3.0: Being Human in the Age of Artificial Intelligence,” PENGUIN UK (July 18, 2018), ISBN 978-0141981802
• Tom Chivers, “The AI Does Not Hate You,” Orion Publishing Group (June 13, 2019), ISBN 978-1474608787
• Other articles and self-reading material

GE3
Science & Engineering
Scientific Literacy

Potential Course Overlap:
Course overlaps ECS 111 and ECS 170 in the coverage of AI basics; however, both ECS 111 and ECS 170 cover in much greater detail the application and/or implementation of AI. Course overlaps with ECS 171, EEC 174AY, EEC 174BY, EEC 179, and BIM 155 in the coverage of ML basics; however, the other courses cover in much greater detail the application and/or implementation of ML. Students completing this course will have a foundational understanding of AI/ML, but will not be yet equipped to apply its use or implement it as students that have completed any of the mentioned overlap courses.

Summary of Course Content:

1. Introduction and Course Overview
2. Going Digital: Representing different types of information in a computer
   1. Representations of numbers: from integers and real numbers to binary formats
   2. Analog vs digital signal. Sampling and discretization
   3. Representing music, pictures, and movies in a computer
   4. Computer hardware: processors, interfaces, storage
   5. Basic principles of computer operations
3. The logic behind computing
   1. Propositional and first order logic
   2. Basic set theory
   3. Methods of proof. Induction and recursion. Inductive definitions.
   4. Combinatorics and counting. Permutations, combinations. The Pigeonhole principle.
4. Introduction to Algorithms by examples
   1. Program design
   2. Algorithms: correctness and complexity
   3. Basic algorithms: sorting and searching
5. Ethical issues: computer and society
   1. Data collection and privacy
   2. Data forgery and deep fakes

Illustrative Reading:

• S. S. Epp, Discrete Mathematics with Applications, 5th edition, Cengage Learning, 2019.
• K. Rosen, Discrete Mathematics and Its Applications, 7th edition, McGraw-Hill, 2011.
• M. Lipson, Schaum’s Outline of Discrete Mathematics, revised 3rd edition, 2009.
• Selected review papers and technical papers and class notes will be used.

Potential Course Overlap:
The course overlaps with ECS 20 (Discrete Mathematics for Computer Science) but this course does not include asymptotic notation, combinatorics, discrete probability, graphs, or trees. The course overlaps with MAT 108 (Introduction to Abstract Math) but this course does not include topics like cardinal numbers. ECS 17 includes topics on ethical issues related to computer uses which neither ECS 20 nor MAT 108 cover. ECS 17 is a fundamental, preparatory course for Data Science majors.

Summary of Course Content

1. Programming in C
   1. Basic syntax and difference of C and other languages such as Python.
   2. Data types: scalar, single and multidimensional arrays, character strings, structs.
   3. Use of system files such as library and “include” files.
   4. Use of I/O including the use of file manipulation and command line arguments.
   5. Pointers and dynamic memory allocation.
   6. Functions: declaration and calls, & and * operators, parameters, function call stacks.
   7. Modular programming: programs composed of multiple C files and headers, overview of the compilation/linking sequence
2. The UNIX Environment
   1. First-level understanding of the nature of UNIX processes and job control.
   2. UNIX hierarchical file system.
   3. UNIX utilities (e.g. find, grep, sort, uniq, head, etc.).
3. Software Engineering & Tools
   1. Version Control (e.g. git).
   2. Program development and use of the UNIX “make” program to organize them.
   3. Software debugging techniques.
   4. Other Tools (e.g. IDEs, ssh clients, linters, etc.).
4. Implementation of Data Structures
   1. Singly-and doubly-linked lists.
   2. Stacks and queues.
   3. Trees (e.g. binary, AVL, Red-Black etc.).

Illustrative Reading
J. Hanley and E. Koffman, Problem Solving and Program Design in C, 8th edition, Pearson, 2015.
W. Shotts, The Linux Command Line 2nd Edition, No Starch Press, 2019.

GE3
Science & Engineering

Overlap
The course overlaps with ECS 36A and ECS 34 in the coverage of UNIX but does not include shell scripting. The course overlaps with ECS 34 and ECS 36B in the coverage of version control and ECS 36B in the implementation of some data structures; however, ECS 34 and ECS 36B cover the implementations in C++. The course overlaps with ECS 36C in the implementation of some more advanced data structures but ECS 36C implementations are in C++.

Summary of Course Content
Student facilitated course intended for undergraduate students, under the supervision of a faculty mentor. Course content and structure vary.

Illustrative Reading
Varies by topic

Potential Course Overlap
None

Illustrative Reading
Varies by course offering in the special topic areas

GE3
Science & Engineering

Summary of Course Contents

1. Basic Memory Architecture
   Review of ECS 40 C-language material on bits, bytes and memory addresses. Linear versus segmented address forms.
2. Introduction to Processor Architecture
   Introduction to instruction sets, addressing modes and register sets, and their variation from one machine to another. Comparison of at least one CISC architecture and at least one RISC architecture, via extensive assembly-language programming on each. Memory-mapped versus separate-address-space I/O. I/O devices. Hardware interrupts. I/O programming—wait-loop, interrupt-driven, and via calls to operating system services or keyboard, video and file access.
3. The Role of System Software in Producing an Abstract Machine
   Distinction between the roles of hardware and software. The use of compilers and operating systems in providing abstractions and machine independence to the programmer. Compiler implementation of C/Pascal data types, and storage allocation of variables in memory. Role of the operating system in helping the programmer to create, store and execute his/her programs, and in managing system resources.
4. Machine Capability and Speed
   Transportability/nontransportability of programs on different machines, and under different operating systems and compilers. Efficiency of compiled vs. hand-coded programs. Writing mixed C-language/assembly language programs for extra efficiency or for special capabilities. Tradeoffs between the speed of hardware implementation of a function and the flexibility of software implementation.

Goals
Students will (1) learn fundamental concepts of computer architecture; (2) learn fundamental concepts of software systems; (3) be able to write programs in assembly language for at least two different architectures .

Illustrative Reading

• Norm Matloff, Below C Level: An Introduction to Computer Systems, 2012. Open-source book, available at http://heather.cs.ucdavis.edu/~matloff/50/PLN/CompSystsBook.pdf
• Gerald Karam and John Bryant, Principles of Computer Systems, Prentice Hall, 1992

Engineering Design Statement
Students in the course write sophisticated, systems-level programs according to their own design. Many of the programs involve analysis of tradeoffs between efficient use of the hardware and ease of programming.

ABET Category Content
Engineering Science: 2 units
Engineering Design: 2 units

GE3
Science & Engineering

Overlap
Substantial overlap with EEC 70, but large student demand justifies the overlap in content.

Outcomes