Introduction to Artificial Intelligence | BS in Applied Computer Science

Course Details

*Note: This course description is only applicable for the Computer Science Post-Baccalaureate program. Additionally, students must always refer to course syllabus for the most up to date information.

Credits: 3.0
Prerequisites: Prerequisite of CSPB/CSCI 2270, CSPB/CSCI 2824, and CSPB/CSCI 3022, all with minimum grade C-.
Minimum Passing Grade: C-
Textbook: Artificial Intelligence: A Modern Approach 3rd Ed. by Peter Norvig and Stuart J. Russell

Brief Description of Course Content

Surveys artificial intelligence techniques of search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language.

Specific Goals

Specific Outcomes of Instruction

An ability to explain what AI is about, what it can solve, its brief history and applications, and its social impact.
An ability to explain key concepts such as agents, environment and how the type of the agent and the environment affect the choice of an algorithm
An ability to explain how each AI algorithm works and implement those in codes.
An ability to explain the algorithm properties such as completeness, optimality, time and space complexity and can compare algorithm efficiencies.
Determine suitable AI algorithms to apply to a specific problem.

Brief List of Topics to be Covered

Search
- Classical search: DFS, BPS, iterative deepening, UCS
- Non-classical search: heuristics, greedy search, A*
- CSP
- Adversarial Search
Probabilistic Search
- MDP
- Reinforcement Learning
- BayesNets, HMM
Machine learning in AI
- Machine learning basics
- Logistic regression, perceptron, ANN, other ML models (e.g. decision tree)
- Deep learning, applications in computer vision, NLP, robotics

Mathematical Concepts Used

Data Structures: Queue, stack, tree, graph
Probability Basics: Bayes Rule, conditional probability, joint probability
Basic Math Functions: Logarithm, exponent, argmax/argmin, max/min
Basic Calculus: concept of partial differentiation, gradient, chain rule

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CSPB 3202 - Introduction to Artificial Intelligence

Course Details

Brief Description of Course Content

Specific Goals