Author: csed

CE-CAE2-Relevant tools, standards, and/or engineering constraints

CS Core:

KA Core:

Non-core:

Core Learning Outcomes:

1. Describe at least two common types of circuit simulators and contrast the advantages and applications of each.
2. Interpret issues associated with interfacing digital computer systems with an analog world, including the use of standard data
   conversion circuits.
3. Summarize the role of standards in compatibility, interconnection, and safety of systems.
4. Articulate the purpose of buses and other interconnection and communication networks.
5. Illustrate the role of constraints, parameters, and tradeoffs in electronic circuit design.

Suggestions Accepted for consideration for the next Edition:

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.

CE-CAE1-History and Overview

CS Core:

KA Core:

Non-core:

Core Learning Outcomes:

1. Describe ways in which computer engineering uses or benefits from electronic devices and circuits.
2. Identify some contributors to circuits and electronics and relate their achievements to this knowledge area.
3. Explain the key differences between analog and digital systems, their implementations, and methods for approximating digital behavior
   with analog systems.
4. Summarize basic electrical quantities and elements that show the relationship between current and voltage.
5. Describe the use of the transistor as an amplifier and as a switch.
6. Explain the historical progression from discrete devices to integrated circuits to current state-of-the-art electronics.

Suggestions Accepted for consideration for the next Edition:

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.

Knowledge Areas and Core Hour allocation

Knowledge Area	No. of Knowledge Units	Core Hours
CE-CAE Circuits and Electronics	12	50
CE-CAL Computing Algorithms	10	30
CE-CAO Computer Architecture and Organization	11	60
CE-DIG Digital Design	11	50
CE-ESY Embedded Systems	13	40
CE-NWK Computer Networks	11	20
CE-PPP Preparation for Professional Practice	11	20
CE-SEC Information Security	11	20
CE-SGP Signal Processing	11	30
CE-SPE Systems and Project Engineering	12	35
CE-SRM System Resource Management	8	20
CE-SWD Software Design	14	45
Total	135	420

Suggestions Accepted for consideration for the next Edition:

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.

Applications Competency Statements for Applications:

	Design	Develop	Document	Evaluate	Humanize
Input
Computation				AI
Output
Platform
Other

Competency Model for CS2023

• Software
• Systems
• Applications

Competency Statement Example

Task: Compare the performance of three supervised learning models on a dataset

Competency statement: Use three supervised learning algorithms to compare the efficiency of training, coverage of example and overfitting issues on a dataset.

Required knowledge:

• AR-Fundamentals: Artificial Intelligence – Fundamentals
• AR-ML: Artificial Intelligence – Machine Learning

Required skills: Apply, Evaluate

Desirable professional dispositions: Meticulous, Persistent

	Knowledge Area	# Knowledge Units	CS Core Hours	KA Core Hours
AI	Artificial Intelligence	12	12	18
AL	Algorithmic Foundations	5	32	32
AR	Architecture and Organization	11	9	16
DM	Data Management	13	10	26
FPL	Foundations of Programming Languages	22	21	19
GIT	Graphics and Interactive Techniques	12	4	70
HCI	Human-Computer Interaction	6	8	16
MSF	Mathematical and Statistical Foundations	5	55	145
NC	Networking and Communication	8	7	24
OS	Operating Systems	14	8	13
PDC	Parallel and Distributed Computing	5	9	26
SDF	Software Development Fundamentals	5	43
SE	Software Engineering	9	6	21
SEC	Security	7	6	35
SEP	Society, Ethics, and the Profession	11	18	14
SF	Systems Fundamentals	9	18	8
SPD	Specialized Platform Development	8	4
	Total	162	270	N/A

Suggestions Accepted for consideration for the next Edition:

1. Core Hours for Artificial Intelligence should be increased

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.

Knowledge Units and Core Hour allocation

Knowledge Unit	CS Core	KA Core
Fundamental Issues	2	1
Search	2 + 3 (AL)	6
Fundamental Knowledge Representation and Reasoning	1 + 1 (MSF)	2
Machine Learning	4	6
Applications and Societal Impact	3	3
Probabilistic Representation and Reasoning
Planning
Logical Representation and Reasoning
Agents and Cognitive Systems
Natural Language Processing
Robotics
Perception and Computer Vision
Total	12	18

Suggestions Accepted for consideration for the next Edition:

1. Core Hours for Machine Learning should be increased

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.

AI-Introduction: Fundamental Issues

CS Core:

1. Overview of AI problems, Examples of successful recent AI applications
2. Definitions of agents with examples (e.g., reactive, deliberative)
3. What is intelligent behavior?
   1. The Turing test and its flaws
   2. Multimodal input and output
   3. Simulation of intelligent behavior
   4. Rational versus non-rational reasoning
4. Problem characteristics
   1. Fully versus partially observable
   2. Single versus multi-agent
   3. Deterministic versus stochastic
   4. Static versus dynamic
   5. Discrete versus continuous
5. Nature of agents
   1. Autonomous, semi-autonomous, mixed-initiative autonomy
   2. Reflexive, goal-based, and utility-based
   3. Decision making under uncertainty and with incomplete information
   4. The importance of perception and environmental interactions
   5. Learning-based agents
   6. Embodied agents
   7.         sensors, dynamics, effectors
6. Overview of AI Applications, growth, and impact (economic, societal, ethics)

KA Core:

7. Practice identifying problem characteristics in example environments
8. Additional depth on nature of agents with examples
9. Additional depth on AI Applications, Growth, and Impact (economic, societal, ethics, security)

Non-core:

10. Philosophical issues
11. History of AI

Illustrative Learning Outcomes:

1. Describe the Turing test and the “Chinese Room” thought experiment.
2. Differentiate between optimal reasoning/behavior and human-like reasoning/behavior.
3. Differentiate the terms: AI, machine learning, and deep learning.
4. Enumerate the characteristics of a specific problem.

Suggestions Accepted for consideration for the next Edition:

1. The role of GenAI in Society

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.

AI-Search: Search

CS Core:

1. State space representation of a problem
   1. Specifying states, goals, and operators
   2. Factoring states into representations (hypothesis spaces)
   3. Problem solving by graph search
      1.         e.g., Graphs as a space, and tree traversals as exploration of that space
      2.         Dynamic construction of the graph (not given upfront)
2. Uninformed graph search for problem solving (See also: AL-Foundational)
   1. Breadth-first search
   2. Depth-first search
      1.         With iterative deepening
   3. Uniform cost search
3. Heuristic graph search for problem solving (See also: AL-Strategies)
   1. Heuristic construction and admissibility
   2. Hill-climbing
   3. Local minima and the search landscape
      1.         Local vs global solutions
   4. Greedy best-first search
   5. A* search
4. Space and time complexities of graph search algorithms

KA Core:

5. Bidirectional search
6. Beam search
7. Two-player adversarial games
   5. Minimax search
   6. Alpha-beta pruning
      5.         Ply cutoff
8. Implementation of A* search
9. Constraint satisfaction

Non-core:

10. Understanding the search space
    10. Constructing search trees
    11. Dynamic search spaces
    12. Combinatorial explosion of search space
    13. Search space topology (e.g., ridges, saddle points, local minima)
11. Local search
12. Tabu search
13. Variations on A* (IDA*, SMA*, RBFS)
14. Two-player adversarial games
    10. The horizon effect
    11. Opening playbooks/endgame solutions
    12. What it means to “solve” a game (e.g., checkers)
15. Implementation of minimax search, beam search
16. Expectimax search (MDP-solving) and chance nodes
17. Stochastic search
    10. Simulated annealing
    11. Genetic algorithms
    12. Monte-Carlo tree search

Illustrative Learning Outcomes:

1. Design the state space representation for a puzzle (e.g., N-queens or 3-jug problem)
2. Select and implement an appropriate uninformed search algorithm for a problem (e.g., tic-tac-toe), and characterize its time and space complexities.
3. Select and implement an appropriate informed search algorithm for a problem after designing a helpful heuristic function (e.g., a robot navigating a 2D gridworld).
4. Evaluate whether a heuristic for a given problem is admissible/can guarantee an optimal solution.
5. Apply minimax search in a two-player adversarial game (e.g., connect four), using heuristic evaluation at a particular depth to compute the scores to back up. [KA Core]
6. Design and implement a genetic algorithm solution to a problem.
7. Design and implement a simulated annealing schedule to avoid local minima in a problem.
8. Design and implement A*/beam search to solve a problem, and compare it against other search algorithms in terms of the solution cost, number of nodes expanded, etc.
9. Apply minimax search with alpha-beta pruning to prune search space in a two-player adversarial game (e.g., connect four).
10. Compare and contrast genetic algorithms with classic search techniques, explaining when it is most appropriate to use a genetic algorithm to learn a model versus other forms of optimization (e.g., gradient descent).
11. Compare and contrast various heuristic searches vis-a-vis applicability to a given problem.
12. ​​Model a logic or Sudoku puzzle as a constraint satisfaction problem, solve it with backtrack search, and determine how much arc consistency can reduce the search space.

Suggestions Accepted for consideration for the next Edition:

1. Time complexity of search algorithms

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.