Foundations of Artificial Intelligence

Course details

The "Foundations of Artificial Intelligence" course offers an in-depth understanding of the fundamental concepts that power AI. Students will explore the history, evolution, and principles that have shaped AI and will engage in theoretical, practical, and ethical discussions surrounding this transformative technology. From problem-solving algorithms to intelligent agents, this course will empower students to navigate and innovate in the burgeoning field of AI.

Start Date

TBD

Duration

14-15 weeks

Prerequisites

Basic knowledge in programming, data structures, and algorithms.

Learning Objectives

Understand the history and evolution of AI.
Explore classic AI algorithms and problem-solving techniques.
Study intelligent agents and their environments.
Analyze the ethical implications of AI applications.
Develop foundational AI applications using popular frameworks and tools.
Engage in critical discourse about the future trajectories of AI technologies.

Key Features

Labs using popular AI libraries such as TensorFlow and Keras.
In-depth case studies exploring real-world AI applications.
Engage with historical and contemporary AI challenges.
Insights from industry experts through guest lectures.

Learning path

Unit 1: Origins of AI (3)
Unit 2: Fundamental AI Algorithms (5)
Unit 3: Introduction to Machine Learning (4)
Unit 4: Logic and Planning (3)
Unit 5: Reasoning Under Uncertainty (4)
Unit 6: Robotics and Perception (4)
Unit 7: Ethics and Societal Implications of AI (4)
Unit 8: Wrap-up and Review (1)

Topics covered

The Birth of AI: Turing, Minsky, and McCarthy.
Philosophy of AI: Can machines think?
Evolution of AI: From Symbolic AI to Neural Networks.

Search Algorithms: Breadth-first, Depth-first, A*.
Game Playing: Minimax Algorithm, Alpha-beta pruning.
Constraint Satisfaction Problems.
Basics of Probabilistic Reasoning.
Bayesian Networks and Decision Networks.

Supervised vs. Unsupervised Learning.
Decision Trees, Neural Networks, and SVMs.
Ensemble Methods: Random Forests and Gradient Boosting Machines.
Clustering and Dimensionality Reduction.

Propositional and Predicate Logic.
Planning Algorithms: STRIPS, Graphplan.
Dynamic Behavior, Reactive Systems.

Probabilistic Reasoning Systems.
Bayesian Inference and Hidden Markov Models.
Markov Decision Processes.
Reinforcement Learning: Q-learning, Policy Gradients.

Basic Principles of Robotics.
Machine Perception: Vision and Speech.
Motion and Path Planning.
Human-Robot Interaction.

The Ethics of AI: Bias, Fairness, and Accountability.
Societal Implications: Job Automation, Surveillance.
Building Safe and Reliable AI Systems.
The Future of AI: Super Intelligent Systems and Existential Risks.

Course Review and Exploration of Current Research

Reading Material: Combination of standard textbooks, recent research papers, and articles from the domain.

Assignments: Hands-on labs and projects, focusing on real-world problems and data sets.

Faculty: Expert in the fields of AI.

Teaching Instructors: TBD