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AI3102
4 hours English

Advanced AI Techniques

Advanced Artificial Intelligence
1 Quantifying Uncertainty (Definition of Uncertainty, Rational decision, Bayes Rule, etc.)
2 Probabilistic Reasoning (Bayesian Networks, Exact and approximate Inference, Monte Carlo method)
3 Probabilistic Reasoning over Time (Temporal Models, Hidden Markov Model, Dynamic Bayesian network)
4 Decision Making (Simple, Complex, and multiagent decision making)
5 Nature-Inspired Metaheuristics
6 Evolutionary Computation
7 Explainable artificial intelligence
1.1 Mapped to: K1

Understand knowledge representation

Teaching Strategy Lectures, discussions
Assessment Methods Exams
1.2 Mapped to: K1

Understand how to quantify the uncertainty and probabilistic reasoning

Teaching Strategy Lectures, discussions
Assessment Methods Exams
1.3 Mapped to: K1

Understand the decision-making process

Teaching Strategy Lectures, discussions
Assessment Methods Exams
1.4 Mapped to: K1

Understand different types of nature inspired metaheuristic algorithms

Teaching Strategy Lectures, discussions
Assessment Methods Exams
2.1 Mapped to: S1

Implement and apply Probabistic reasoning models to solve real-world problems

Teaching Strategy Lectures, discussions
Assessment Methods Exams, assignments, project
2.2 Mapped to: S2

Implement decision making algorithms

Teaching Strategy Lectures, discussions
Assessment Methods Exams, assignments, project
2.3 Mapped to: S2

Apply bio-inspired metaheuristic algorithms to solve the real-worl problems

Teaching Strategy Lectures, discussions
Assessment Methods Exams, assignments
3.1 Mapped to: V1

Demonstrate ability to apply ethical behavior in AI projects

Teaching Strategy Lectures, discussions
Assessment Methods assignments
3.2 Mapped to: V2

Demonstrate ability to recognize professional responsibilities and make informed judgments in AI practice based on legal and ethical principles.

Teaching Strategy Lectures, discussions
Assessment Methods assignments