What will you learn in Artificial Intelligence Foundations: Logic, Learning, and Beyond Course
Grasp core AI concepts: search, knowledge representation, planning, and learning
Understand machine learning paradigms: supervised, unsupervised, and reinforcement learning
Explore neural networks fundamentals: perceptrons, backpropagation, and deep learning basics
Apply AI techniques to practical problems: classification, clustering, and sequential decision-making
Evaluate AI models using metrics and cross-validation, and understand ethical considerations
Program Overview
Module 1: Foundations of AI
⏳ 1 week
Topics: History of AI, Turing Test, rational agents, PEAS frameworks
Hands-on: Define a PEAS description and implement a simple reflex agent in Python
Module 2: Problem Solving & Search
⏳ 1 week
Topics: Uninformed search (BFS, DFS), informed search (A*, heuristics)
Hands-on: Build and compare BFS vs. A* on path-finding grids
Module 3: Knowledge Representation & Logic
⏳ 1 week
Topics: Propositional and first-order logic, inference, resolution
Hands-on: Encode simple puzzles in propositional logic and solve via resolution
Module 4: Planning & Decision Making
⏳ 1 week
Topics: STRIPS representation, forward/backward planning, Markov Decision Processes
Hands-on: Implement value iteration on a grid-world MDP
Module 5: Machine Learning Basics
⏳ 1 week
Topics: Linear regression, logistic regression, decision trees, overfitting
Hands-on: Train and evaluate models on a public dataset using scikit-learn
Module 6: Unsupervised Learning & Clustering
⏳ 1 week
Topics: K-means, hierarchical clustering, dimensionality reduction (PCA)
Hands-on: Cluster customer data and visualize results with PCA projections
Module 7: Neural Networks & Deep Learning Intro
⏳ 1 week
Topics: Perceptron, multilayer networks, activation functions, backpropagation
Hands-on: Build a two-layer neural network from scratch to classify MNIST digits
Module 8: Reinforcement Learning Basics
⏳ 1 week
Topics: Exploration vs. exploitation, Q-learning, policy gradients overview
Hands-on: Implement Q-learning for a simple OpenAI Gym environment
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Job Outlook
AI Fundamentals are critical for roles like AI Engineer, Data Scientist, and Research Associate
Foundational knowledge opens doors in tech, healthcare, finance, and robotics industries
Salaries for entry-level AI positions typically start around $85,000, rising to $150,000+ with experience
Strong base for advanced AI specializations in NLP, computer vision, and reinforcement learning
Explore More Learning Paths
Deepen your understanding of AI’s core principles — from logic and reasoning to learning algorithms — with these curated learning paths that complement and enhance your foundational AI knowledge.
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Related Reading
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Specification: Artificial Intelligence Foundations: Logic, Learning, and Beyond Course
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FAQs
- Basic understanding of mathematics (linear algebra, probability) is helpful but not mandatory.
- Programming experience is useful but not required; examples are explained conceptually.
- No prior exposure to AI concepts is needed.
- The course introduces logic, learning, and reasoning fundamentals from scratch.
- Learners can progress step by step with guided examples.
- The course emphasizes theoretical understanding of AI foundations.
- Some conceptual pseudocode may be shown to explain algorithms.
- Practical programming exercises are limited or optional.
- Knowledge gained can be applied later in implementation-focused courses.
- Students develop reasoning skills to design AI systems conceptually.
- Yes, the course focuses on concepts like logic, learning, and problem-solving.
- Technical jargon is explained in an accessible manner.
- Minimal programming knowledge is needed to follow examples.
- Non-technical learners can still understand AI reasoning and decision-making processes.
- It is ideal for managers, analysts, and students curious about AI fundamentals.
- It builds a solid understanding of logic, reasoning, and learning principles.
- Introduces foundational concepts used in machine learning and AI models.
- Prepares learners to understand algorithm design and problem-solving strategies.
- Provides conceptual clarity for advanced AI topics like neural networks or reinforcement learning.
- Strong foundation reduces confusion when tackling more complex AI systems.
- The course primarily focuses on foundational concepts rather than applications.
- Examples illustrate logical reasoning and decision-making processes.
- Concepts are applicable in areas like search algorithms, game AI, and expert systems.
- Learners gain skills to analyze real-world AI problems conceptually.
- Further study or advanced courses are recommended for hands-on AI projects.
