Course Learning Outcomes

Course Learning Outcomes

By the end of this course, students will be able to:

  1. Understand the evolution of artificial intelligence (AI) and machine learning (ML) from their origins to present-day applications.

  2. Gain a strong grasp of fundamental AI and ML concepts, terminologies, and principles.

  3. Identify and explain the practical applications of AI and ML across various sectors.

  4. Develop the ability to communicate AI and ML concepts clearly to diverse audiences.

These outcomes connect directly to the unit sequence. Units 1-2 address outcome 1 (history and foundations) and outcome 2 (core concepts). Units 3-7 deepen outcome 2 (algorithms and methods) and build outcome 3 (real-world applications). Unit 8 and the course wrap-up address all four outcomes together.

Unit-Level Learning Goals

Unit 1: Foundations of AI

  • Define artificial intelligence using multiple conceptual lenses

  • Describe key milestones in the history of AI development

  • Distinguish narrow AI from general AI

  • Identify real-world AI applications and their societal implications

  • Discuss ethical considerations in AI design and deployment

Unit 2: Intelligent Agents

  • Explain the agent-environment interaction loop

  • Apply the PEAS framework to characterize AI agents

  • Classify environment types by their properties

  • Compare simple reflex, model-based, goal-based, and utility-based agent architectures

Unit 3: Search Techniques

  • Formulate real-world problems as state-space search problems

  • Trace the execution of BFS, DFS, and uniform-cost search

  • Implement A* search with an admissible heuristic

  • Evaluate search algorithms by completeness, optimality, time, and space complexity

Unit 4: Optimization and CSPs

  • Explain how local search explores solution spaces without full paths

  • Implement simulated annealing and describe when it avoids local optima

  • Formulate a problem as a constraint satisfaction problem (CSP)

  • Apply backtracking search with arc consistency and heuristics to solve CSPs

Unit 5: Introduction to Logic

  • Translate natural language statements into propositional logic

  • Evaluate logical formulas using truth tables

  • Identify tautologies, contradictions, and logical equivalences

  • Explain why formal logic is useful for AI knowledge representation

Unit 6: Knowledge-Based Agents

  • Describe how a knowledge base stores facts and rules

  • Apply modus ponens and other inference rules

  • Trace forward chaining and backward chaining on a Horn clause knowledge base

  • Build a simple expert system for a specified domain

Unit 7: Probability and Uncertainty

  • Apply the axioms of probability and compute conditional probabilities

  • Use Bayes' theorem to update beliefs given evidence

  • Explain the structure and semantics of a Bayesian network

  • Perform probabilistic inference in a simple Bayesian network

Unit 8: Machine Learning Foundations

  • Distinguish machine learning from symbolic AI approaches

  • Explain the supervised learning framework (training, testing, generalization)

  • Evaluate classifiers using accuracy, precision, recall, and confusion matrices

  • Describe the bias-variance tradeoff and its consequences for model design

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