🤖 Subject

Artificial Intelligence

From the foundations of intelligent systems to deep learning, generative AI, large language models, robotics, and responsible AI governance.

AI Fundamentals

An introduction to how artificial intelligence has been defined, its founding at the 1956 Dartmouth workshop, the Turing test, and the field's major subfields.

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Traces AI's evolution from 1940s formal-neuron models and the 1955 Dartmouth workshop through symbolic expert systems, two AI winters, the connectionist and deep-learning revivals, and today's foundation models and autonomous agents.

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Philosophy of AI and Machine Intelligence

Examines philosophical questions AI raises about intelligence, rationality, agency, and consciousness, distinguishing settled engineering practice from open debates like the Chinese Room and machine consciousness.

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Intelligent Agents and Environments

Introduces the formal agent model — sensors, actuators, PEAS, and rationality — and the environment properties that determine how difficult a given task is for any intelligent agent.

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AI Paradigms and Architectures

Surveys AI's major architectural paradigms — symbolic, probabilistic, connectionist, evolutionary, behavioural, and neuro-symbolic hybrid approaches — and the different assumptions each makes about how intelligence should be built.

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Computational Cognitive Science

Examines how researchers build and test computational models of human perception, memory, learning, reasoning, and language, and where the mind-as-computer analogy holds and where it breaks down.

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AI Problem Formulation and Solution Design

Teaches the discipline of turning an ambiguous real-world challenge into a formally specified problem — states, actions, objectives, constraints, and evaluation criteria — before any algorithm is chosen.

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AI Research Methods and Scientific Practice

Walks through the full research cycle in AI — literature review, hypothesis formation, experiment design, reproducibility practice, and scientific communication — using real conference standards as the model to follow.

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Machine Learning

An introduction to machine learning's formal definition, its three core paradigms, and the methodology used to train and evaluate models reliably.

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Deep Learning

An introduction to artificial neural networks, the backpropagation algorithm that trains them, and convolutional architectures that transformed computer vision.

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Discrete Mathematics for AI

Builds the sets, relations, functions, combinatorics, recurrence-relation, and Boolean-algebra vocabulary that underlies data structures, search-space sizing, and rule-based reasoning in AI, worked through concrete numeric examples.

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Linear Algebra for AI

Builds vectors, matrices, and tensors as representations and transformations, then walks through eigenvalues, eigenvectors, and matrix decomposition using concrete worked numeric examples relevant to AI systems.

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Calculus for Machine Learning

Builds derivatives, gradients, Jacobians, Hessians, and the multivariable chain rule as the calculus toolkit that powers gradient-based learning inside neural networks and other modern AI systems today.

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Probability Theory for AI

Introduces random variables, probability distributions, conditional probability, expectation, and variance as the mathematical language AI systems use to reason carefully under genuine, irreducible uncertainty.

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Statistics and Statistical Inference

Covers estimation, sampling distributions, confidence intervals, hypothesis testing, and regression as the inferential tools for judging whether a measured AI result is genuinely statistically meaningful.

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Bayesian Methods

Develops Bayes' theorem, priors, likelihoods, posterior updating, and Bayesian decision theory as the framework AI systems use to revise beliefs rationally as new evidence arrives.

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Optimization for Artificial Intelligence

Covers convexity, gradient descent, constrained optimization, stochastic optimization, and duality as the mathematical basis for how AI systems efficiently search for good model parameters at scale.

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Information Theory

Introduces Shannon entropy, cross-entropy, mutual information, and information gain as the shared mathematical toolkit behind data compression and feature selection in machine learning.

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Graph Theory and Network Mathematics

Covers graphs, vertices, edges, paths, connectivity, trees, and traversal algorithms as the shared mathematical language behind routing, knowledge graphs, and graph-structured neural networks.

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Decision Theory and Utility

Develops rational-preference axioms, utility functions, expected utility maximization, and risk attitudes as the normative framework decision-theoretic AI agents use to choose actions under uncertainty.

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Game Theory and Strategic Decision-Making

Introduces normal-form games, dominant strategies, and Nash equilibrium, plus cooperative bargaining and mechanism design, as the framework for reasoning about multi-agent AI systems whose payoffs depend on each other's choices.

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Causal Inference and Counterfactual Reasoning

Introduces causal directed acyclic graphs, confounding, the do-operator for interventions, and structural counterfactuals as the formal tools for reasoning about cause and effect beyond correlation.

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Programming for Artificial Intelligence

Teaches Python-based computational thinking, algorithm implementation, systematic debugging with pdb, and testing with unittest as the programming foundation every later AI topic builds on.

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Data Structures and Algorithms

Covers core data structures — arrays, hash tables, trees, and graphs — plus sorting, searching, algorithmic complexity, and how to argue an algorithm is correct rather than merely lucky.

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Software Engineering for AI

Covers modular software design, API design, documentation conventions, automated testing, and version control as the engineering discipline that keeps AI research code maintainable and trustworthy.

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Databases and Data Management

Covers relational data modeling and SQL, indexing and ACID transactions, and NoSQL and vector database models, building the vocabulary to choose storage architecture deliberately for AI systems.

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Data Engineering for AI

Covers data pipeline design — collection, ingestion, transformation, validation, and storage — plus data provenance and lineage as the discipline that keeps AI training data trustworthy and traceable.

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Parallel, Distributed, and Cloud Computing

Explains how GPUs, clusters, containers, orchestration, and cloud services let AI workloads scale computation across many processors and machines efficiently.

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Data Preparation and Exploratory Analysis

Covers how to clean messy data — missing values, outliers, duplicates — and use visualization and transformation to explore and understand a dataset before modeling.

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Feature Engineering and Representation

Explains how raw data is turned into model-ready numeric features through encoding, scaling, selection, transformation, and dimensionality reduction.

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Dataset Design, Annotation, and Quality

Covers sampling and class balance, annotation and inter-annotator agreement, and documentation frameworks that make datasets trustworthy and auditable.

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Privacy-Preserving Data and Synthetic Data

Covers anonymization, consent, data minimization, differential privacy, and synthetic data generation as approaches to sharing useful data while protecting individuals.

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State-Space Representation and Problem Solving

Introduces the formal state-space model of problem solving — states, actions, transition models, goal tests, and path costs — that every classical search algorithm operates on.

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Uninformed Search Algorithms

Covers the uninformed (blind) search strategies — breadth-first, depth-first, uniform-cost, depth-limited, and iterative-deepening search — that explore a state space using only its structure.

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Heuristic and Informed Search

Introduces informed search — greedy best-first search and A* — along with heuristic design, admissibility, consistency, and memory-bounded variants like IDA*.

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Local and Metaheuristic Optimization

Covers local and metaheuristic optimization — hill climbing and its variants, simulated annealing, tabu search, beam search, and other stochastic optimization strategies.

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Constraint-Satisfaction Problems

Explains how to formulate assignment and scheduling problems as constraint-satisfaction problems and solve them efficiently with backtracking search, ordering heuristics, and constraint propagation.

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Adversarial Search and Game Playing

Covers how agents choose optimal moves against adversaries using minimax and alpha-beta pruning, evaluation functions for practical depth limits, and Monte Carlo tree search for games with huge branching factors.

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Automated Planning and Scheduling

Introduces classical planning representations (STRIPS, PDDL), planning-graph and hierarchical planning techniques, and how a validated plan is scheduled against limited time and resources.

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Planning and Decision-Making Under Uncertainty

Explains how planning extends beyond the classical fully-observable, deterministic setting to contingent plans, probabilistic MDP-based planning, partial observability, and execution-time replanning.

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Generative AI

An introduction to generative modeling, contrasting it with discriminative modeling, and a tour of three foundational architectures: GANs, VAEs, and diffusion models.

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Natural Language Processing

Traces NLP from the classic annotation pipeline of tokenization, tagging, and parsing to distributional word embeddings and today's dominant Transformer-based large language models.

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Computer Vision

An introduction to how machines interpret images, covering the classification, detection, and segmentation task taxonomy plus the ImageNet-driven rise of deep convolutional networks.

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Large Language Models

An introduction to the Transformer architecture and the pretrain-then-adapt paradigm that together underpin modern large language models like GPT-3 and ChatGPT.

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Responsible AI

A capstone survey of how organizations and governments manage AI risk, covering the NIST AI RMF and the EU AI Act and OECD AI Principles.

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Logic for Artificial Intelligence

Introduces propositional and first-order logic as formal languages for representing knowledge, covering syntax, semantics, entailment, quantifiers, and the boundary between decidable and undecidable inference.

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Automated Reasoning and Theorem Proving

Covers resolution refutation, unification and the most general unifier, and the forward- and backward-chaining strategies that let automated systems search efficiently for proofs over large rule bases.

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Knowledge Representation

Surveys how AI systems formally represent objects, categories, properties, events, actions, time, space, and other agents' beliefs — the ontological building blocks beneath any reasoning system.

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Ontologies and Semantic Technologies

Covers taxonomies, description logics, and the W3C semantic web stack — RDF triples, OWL ontologies, and SPARQL queries — as the standards that let machines share and reason over structured knowledge.

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Knowledge Graphs

Introduces knowledge graphs as directed labeled graphs of entities and relations, covering how they are built and curated, how embeddings turn discrete triples into vectors for learning, and how querying, completion, and reasoning operate over inherently incomplete graphs.

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Expert Systems and Rule-Based AI

Covers the architecture of expert systems — knowledge bases and production rules, forward- and backward-chaining inference engines, explanation facilities, and the knowledge-acquisition process — through the landmark case of MYCIN.

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Commonsense and Non-Monotonic Reasoning

Examines reasoning that draws default conclusions from incomplete information and revises them when exceptions appear, covering circumscription, default logic, belief revision, and temporal reasoning as formal answers to the problem of non-monotonicity.

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Fuzzy Logic and Approximate Reasoning

Introduces fuzzy sets, membership functions, and linguistic variables, then walks through Mamdani-style fuzzy rule inference and the historic 1975 fuzzy steam-engine controller that first demonstrated the approach's practical value.

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Neuro-Symbolic Artificial Intelligence

Examines how neuro-symbolic AI combines neural networks' pattern-learning strengths with symbolic reasoning's compositional guarantees, using AlphaGeometry's verified Olympiad-level results as a concrete case study while honestly framing the field's genuinely open research challenges.

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Probabilistic Reasoning and Inference

Covers conditional probability, Bayes' rule, conditional independence, and Bayesian network structure, then extends to likelihoods and probabilistic decision-making as the mathematical foundation for reasoning under genuine uncertainty.

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Bayesian Networks and Graphical Models

Introduces directed and undirected probabilistic graphical models, showing how conditional independence structure enables exact inference algorithms like variable elimination, belief propagation, and junction trees, plus principled approximate methods when exact computation becomes intractable.

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Sequential Probabilistic Models

Covers Markov chains, hidden Markov models, dynamic Bayesian networks, and state-space models as the core toolkit for reasoning about hidden state that evolves over time and is only ever observed indirectly.

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Monte Carlo and Sampling Methods

Covers importance sampling, Markov chain Monte Carlo (Metropolis-Hastings and Gibbs sampling), particle filtering, and simulation as tools for approximating intractable probabilistic computations with random samples.

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Probabilistic Programming

Explores how probabilistic programming languages express generative models as executable code with sampling and conditioning primitives, letting general-purpose inference engines automate Bayesian computation that would otherwise require bespoke derivations.

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Supervised Learning: Regression

Covers linear regression and the least-squares objective, regularized regression via ridge and lasso, and nonlinear and probabilistic approaches including locally weighted regression and the maximum-likelihood view of least squares.

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Supervised Learning: Classification

Covers logistic regression as a discriminative classifier, k-nearest neighbors as an instance-based method, and naive Bayes as a generative classifier built on a conditional-independence assumption.

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Decision Trees and Ensemble Learning

Covers how decision trees recursively split data using impurity measures, how bagging and random forests combine many trees to reduce variance, and how boosting and stacking combine diverse models to reduce error further.

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Support Vector Machines and Kernel Methods

Covers the maximum-margin principle behind support vector machines, the kernel trick that lets them draw nonlinear decision boundaries without ever computing a high-dimensional feature mapping explicitly, and support vector regression.

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Unsupervised Learning and Clustering

Surveys the major families of clustering algorithms — partitional k-means, hierarchical, probabilistic mixture models fit with EM, density-based DBSCAN, and spectral clustering — and the differing assumptions each makes about cluster shape and structure.

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