Lectures (Tom Mitchell and Maria-Florina Balcan)

Lecture	Topics	Readings and useful links	Handouts
Intro to ML Decision Trees	Machine learning examples Well defined machine learning problem Decision tree learning	Mitchell: Ch 3 Bishop: Ch 14.4 The Discipline of Machine Learning	Slides
Decision Tree learning Review of Probability	The big picture Overfitting Random variables and probabilities	Mitchell: Ch 3 Andrew Moore's Basic Probability Tutorial	Slides Annotated Slides
Probability and Estimation	Bayes rule MLE MAP	Mitchell: Estimating Probabilities	Slides Annotated Slides
Naive Bayes	Conditional Independence Naive Bayes: why and how	Mitchell: Naive Bayes and Logistic Regression	Slides Annotated Slides
Gaussian Naive Bayes	Gaussian Bayes classifiers Document Classification Brain image classification Form of decision surfaces	Mitchell: Naive Bayes and Logistic Regression	Slides Annotated Slides
Logistic Regression	Naive Bayes - the big picture Logistic Regression: Maximizing conditional likelihood Gradient ascent as a general learning/optimization method	Mitchell: Naive Bayes and Logistic Regression	Slides Annotated Slides
Linear Regression	Generative/Discriminative models Minimizing squared error and maximizing data likelihood Regularization Bias-variance decomposition		Slides Annotated Slides
Learning Theory I	Distributional Learning PAC and Statistical Learning Theory Sample Complexity	Mitchell: Ch 7 Notes on Generalization Guarantees	Slides
Learning Theory II	Sample Complexity Shattering and VC Dimension Sauer's Lemma	Mitchell: Ch 7 Notes on Generalization Guarantees	Slides
Learning Theory III	Rademacher Complexity Overfitting and Regularization		Slides
Graphical Models I	Bayes Nets Representing joint distributions with conditional independence assumptions	Bishop chapter 8, through 8.2	Slides Annotated Slides
Graphical Models II	Inference Learning from fully observed data Learning from partially observed data		Annotated Slides
Graphical Models III	EM Semi-supervised learning	Bishop Chapter 8 Mitchell Chapter 6	Slides Annotated Slides
Exam #1
EM and Clustering	Mixture of Gaussian clustering K-means clustering	Bishop Chapter 8 Mitchell Chapter 6	Slides Annotated Slides
Spring Break
Boosting	Weak vs Strong (PAC) Learning Boosting Accuracy Adaboost	The Boosting Approach to Machine Learning: An Overview Theory and Applications of Boosting (NIPS Tutorial)	Slides
Adaboost, Margins, Perceptron	Adaboost: Generalization Guarantees(naive and margins based). Geometric Margins and Perceptron	Notes on Perceptron	Slides Slides (PPT)
Kernels	Geometric Margins Kernels: Kernelizing a Learning Algorithm Kernelized Perceptron	Bishop 6.1 and 6.2	Slides
SVM	Geometric Margins SVM: Primal and Dual Forms Kernelizing SVM Semi-supervised Learning Semi-supervised SVM	Notes on SVM by Andrew Ng	Slides
Semi-supervised Learning	Transductive SVM Co-training and Multi-view Learning Graph-based Methods	"Semi-Supervised Learning" in Encyclopedia of Machine Learning Transductive SVM Paper	Slides
Active Learning	Batch Active Learning Selective Sampling and Active Learning Sampling Bias	Two Faces of Active Learning Active Learning Literature Survey (by Burr Settles) Active Learning Survey (by Balcan and Urner)	Slides
Partitional Clustering Hierarchical Clustering	k-means, Lloyd's method, k-means++ Agglomerative Clustering	Hastie, Tibshirani and Friedman, Chapter 14.3 Center Based Clustering: A Foundational Perspective	Slides
Learning Representations Dimensionality Reduction	Principal Component Analysis Kernel Principal Component Analysis	Bishop 12.1, 12.3	Slides
Never Ending Learning			Slides
Neural Networks Deep Learning		Mitchell, Chapter 4	Slides
Reinforcement Learning	Markov Decision Processes Value Iteration Q-learning	Mitchell, Chapter 13 Kaelbling, et al., Reinforcement Learning: A Survey	Slides
Deep Learning Differential Privacy Discussion on the Future of ML			Slides (Privacy) Slides (Deep Nets)

Andrew Ng's review notes on:

• Linear Algebra
• Probability Theory
• Multivariate Gaussian - I
• Multivariate Gaussian - II
• Convex Optimization - I
• Convex Optimization - II
• Hidden Markov Models

Lecture Notes

• Andrew Ng's notes on Loss Functions
• Andrew Ng's course notes on Linear and Logistic Regression.
• The Theory Behind Overfitting, Cross Validation, Regularization, Bagging and Boosting: Tutorial by Ghojogh and Crowley.
• Andrew Ng's notes on Naive Bayes 
• Andrew Ng's notes on SVM and Kernel Methods
• Andrew Ng's notes on Neural Networks and Deep Learning
• [Practical Tips] - Efficient Backprop by Yann LeCun
• Vapnik's paper on "Principles of Risk Minimization for Learning Theory", NIPS 1992
• Andrew Ng's  ML Review Notes 
• Spectral Methods for Dimensionality Reduction
• Nonlinear Dimensionality Reduction by Locally Linear Embedding
• MATLAB Workshop 2: An introduction to Support Vector Machine implementations in MATLAB
• Neighbourhood Components Analysis
• A Global Geometric Framework for Nonlinear Dimensionality Reduction
• Graphics Principles Cheat Sheet
• Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
• Exponential Families
• Variational Inference: A Review for Statisticians
• Foundations of Data Science
• Optimization Methods for Large-Scale Machine Learning
• Convex Optimization
• Theoretical Computer Science Cheat Sheet
• Entropy, Relative Entropy, And Mutual Information
• Information Theory And Statistics
• Lecture #7: Understanding and Using Principal Component Analysis (PCA)
• Lecture #9: The Singular Value Decomposition (SVD) and Low-Rank Matrix Approximations
• CS224n: Natural Language Processing with Deep Learning (Lecture Notes: Part I)
• Super VIP Cheatsheet: Machine Learning
• CSE176 Introduction to Machine Learning — Lecture notes
• Linear Algebra Review and Reference
• Lecture notes (Roger Grosse)
• Data Mining, Inference, and Prediction
• A Primer on Neural Network Models for Natural Language Processing
• Machine Learning and Data Mining Lecture Notes
• Common tests are linear models
• Linear Algebra Abridged
• The Matrix Calculus You Need For Deep Learning
• Measure, Integration and Real Analysis
• Concise Machine Learning
• Machine Learning Basic Concepts
• Notation: Overview
• Lecture Notes 1: Vector spaces
• Probability Cheatsheet
• An overview of gradient descent optimization algorithms
• Lecture 0: Course Introduction
• A Structural Approach to Selection Bias
• Troubleshooting Deep Neural Networks: A Field Guide to Fixing Your Model
• Introduction to Applied Linear Algebra
• Python For Data Science Cheat Sheet (NumPy Basics)
• Data Wrangling with pandas Cheat Sheet
• Python For Data Science Cheat Sheet (Scikit-Learn)
• Python For Data Science Cheat Sheet (Keras)
• Data Wrangling with dplyr and tidyr Cheat Sheet
• Data Visualization with ggplot2 Cheat Sheet
• CS725 : Foundations of Machine learning - Lecture Notes
• Lecture Notes: Optimization for Machine Learning
• CS446: Machine Learning Lecture Notes
• Data Science Lecture Notes
• Lectures on Numerical Methods for Data Science

C19 ML lectures [Andrew Zisserman]

• Lecture 1 "Introduction"

• Lecture 2 "The SVM Classifier"

• Lecture 3 "SVM dual, kernels and regression"

• Lecture 4 "Regression continued and multiple classes"

• Examples sheet

ML Lecture Notes (Prof. Qiangfu Zhao, Prof. Yong Liu and Prof. Yuichi Yaguchi)

Contents

History of AI and ML

Fundamentals of machine learning

Introduction to concept learning

Basic statistic learning

Bayesian network

Project-I

Multilayer perceptron

Convolutional neural network

Autoencoder

Restricted Boltzmann machine

Decision trees

Project-II

Statistical ML Lecture Notes [Thomas Schön]

• [pdf] Introduction (notes)
• [pdf] Linear regression 
• [pdf] Linear classification 
• [pdf] Neural networks, kernel methods intro. 
• [pdf] Kernel methods 
• [pdf] EM and clustering (Notes)
• [pdf] Approximate inference (Notes)
• [pdf] Graphical models 
• [pdf] Graphical models and message passing 
• [pdf] MCMC and sampling methods 
• [pdf] Bayesian nonparametric models 

Papers

• Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Synthesizing Robust Adversarial Examples
• Robust Physical-World Attacks on Deep Learning Visual Classification
• Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
• PoTrojan: powerful neuron-level trojan designs in deep learning models
• Semantic Adversarial Examples
• Programmable Neural Network Trojan for Pre-Trained Feature Extractor
• STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
• A General Framework for Adversarial Examples with Objectives
• Adversarial Attacks on Neural Network Policies
• Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
• Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
• Analyzing Federated Learning through an Adversarial Lens
• Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
• Generating Natural Language Adversarial Examples
• Sparse Bayesian Adversarial Learning Using Relevance Vector Machine Ensembles
• ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
• Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
• Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
• On Evaluating Adversarial Robustness
• Adversarial Support Vector Machine Learning
• Trojaning Attack on Neural Networks
• Learning Conjunctive Concepts in Structural Domains
• Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
• Approximation and estimation bounds for artificial neural networks
• Training a 3-Node Neural Network is NP-Complete
• Online Passive-Aggressive Algorithms
• Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
• Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework
• The Perceptron algorithm versus Winnow: linear versus logarithmic mistake bounds when few input variables are relevant
• Large Margin Classification Using the Perceptron Algorithm
• Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm
• General Convergence Results for Linear Discriminant Updates
• Learning representations by back-propagating errors
• Efficient Learning of Linear Perceptrons
• On the Computational Efficiency of Training Neural Networks
• Learnability and the Vapnik-Chervonenkis Dimension
• Computational Limitations on Learning from Examples
• Learning Decision Lists
• A Theory of the Learnable
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• On-Line Algorithms in Machine Learning
• Neural Programmer-Interpreters
• Dueling Network Architectures for Deep Reinforcement Learning
• scikit-learn user guide
• The Matrix Cookbook
• Big Data: New Tricks for Econometrics
• Crash Course on Basic Statistics
• Think Stats: Probability and Statistics for Programmers

ML Lecture Slides [Sargur Srihari]

---

Introduction Machine Learning-Overview(28MB) Python and ML Frameworks(13.9MB) Linear Algebra(4.5MB)  Example: Curve Fitting(934KB) Probability Theory(4.9MB) Numerical Computation(1.4MB) Decision-Theory(488KB) Information Theory(715KB) Probability Distributions Discrete Distributions(1MB) Gaussian Distribution(833KB) Gaussian Bayesian Networks(738KB) Linear Models for Regression Regression with Basis Functions(7.3MB) Gradient Descent(3.2MB) Bias-Variance(950KB) Bayesian Regression(2.5MB) Bayesian Model Comparison(478KB) Evidence Approximation(746KB) Example: Computer Science Ranking(126KB) Linear Models for Classification Overview(4.6MB) Discriminant Functions(5MB) Probabilistic Generative Models(1.3MB) Probabilistic Discriminative Models Fixed Basis Functions(254KB) Logistic Regression(3.6MB) Iterative Reweighted Least Squares(5.1MB) Multiclass Logistic Regression(4.6MB) Probit Regression(356KB) Canonical Link Functions(263KB) Laplace Approximation (1.3MB) Bayesian Logistic Regression(1.1MB) Variational Bayesian Logistic Regression(3.3MB)  Neural Networks Biology(4.5MB)  Feed-forward Network Functions(5.3MB) Network Training(2.6MB) Backpropagation(8.7MB) The Hessian Matrix(562KB) Regularization in Neural Networks Norm Penalty: Bayesian Interpretation(1.2MB) Convolutional Networks(4.9MB) Soft Weight Sharing(1.2MB) Mixture Density Networks (634KB) Bayesian Neural Networks(716KB) Deep Learning Overview(5.2MB) Kernel Methods Kernel Methods(6.3MB) Radial Basis Function Networks(812KB) Gaussian Processes(6.8MB) Sparse Kernel Machines Support Vector Machines(5.4MB) SVM for Overlapping Distributions(1.3MB) Multiclass SVMs (1.4MB) Relation to Logistic Regression (446KB) Mixture Models and EM Unsupervised Learning(1.9MB) K-means Clustering(1.4MB) Gaussian Mixture Models(1.5MB) Latent Variable View of EM(1.1MB) Bernoulli Mixture Models(3.1MB) Theoretical Basis of EM(693KB) Approximate Inference Approximate Inference(180KB) Variational Inference(3.3MB) Variational Mixture of Gaussians(1MB) Sampling Methods Need for Sampling (6.6MB) Basic Sampling Methods(2.5MB) Markov Chain Monte Carlo Sampling(815KB) Gibbs Sampling(1.2MB) Sequential Data Markov Models(2.5MB) Hidden Markov Models(3.1MB) Maximum Likelihood for the HMM(8.5MB) The forward-backward algorithm(15.9MB) Extensions to HMMs(287KB) Linear Dynamical Systems(217KB) Conditional Random Fields(1.6MB) Combining Models Combining Models(pdf, 1.7MB) Bagging(pdf, 675KB) Boosting(pdf, 1.1MB) Tree Models Decision Trees(pdf, 1.9MB) Learning Trees(pdf, 596KB) Random Forests(pdf, 3.4MB) Reinforcement Learning Reinforcement Learning Overview(pdf 4MB) The Learning Task (pdf 1MB) Q-Learning (pdf 6MB) Nondeterministic Q-Learning (pdf 4.9MB) Deep Reinforcement Learning (pdf 3.5MB) AI Ethics AI Ethics(pdf 8.8MB) Trustworthy AI Trustworthy AI(pdf 2.9MB) Explainable AI(pdf 24MB) Explanation by Example(pdf 3.4MB) Deep Explanation(pdf 14.6MB) Causal Explanation(pdf 16.4MB) Concept Learning Hypothesis Space (pdf, 111KB) Candidate Elimination (pdf,236KB) Computational Learning Theory PAC Learning(pdf, 98KB) VC Dimension(pdf, 321KB) Mistake Bound(pdf, 51KB)

ML Course Notes [Nando de Freitas]

---

• Lecture 1: Introduction slides 
• Lecture 2: Linear prediction slides 
• Lecture 3: Maximum likelihood slides.pdf 
• Lectures 4 & 5: Regularizers, basis functions and cross-validation slides.pdf 
• Lecture 6: Optimisation slides.pdf 
• Lecture 7: Logistic regression slides.pdf 
• Lecture 8: Back-propagation and layer-wise design of neural nets slides.pdf 
• Lecture 9: Neural networks and deep learning with Torch slides.pdf 
• Lecture 10: Convolutional neural networks slides.pdf 
• Lecture 11: Max-margin learning and siamese networks slides.pdf 
• Lecture 12: Recurrent neural networks and LSTMs slides.pdf 
• Lecture 15: Reinforcement learning with direct policy search slides.pdf 
• Lecture 16: Reinforcement learning with action-value functions slides.pdf 
• Practical on week 2: (1) Learning Lua and the tensor library. pdf
• Practical on week 3: (2) Online and batch linear regression. pdf
• Practical on week 4: (3) Logistic regression and optimization. pdf
• Practical on week 6: (4) Feedforward neural networks, and implementing your own layer. pdf
• Practical on week 7: (5) Intro to nngraph for graph-shaped modules. pdf
• Practical on week 8: (6) Training a LSTM language model. pdf
• Class on Week 3: Problem set. 
• Class on Week 5: Problem set. 
• Class on Week 7: Problem set. 
• Class on Week 8: Problem set. 

Lectures on Machine Learning (Mark Schmidt)

1. Supervised Learning

• Overview
• Exploratory Data Analysis
• Decision Trees (Notes on Big-O Notation)
• Fundamentals of Learning (Notation Guide)
• Probabilistic Classifiers (Probability Slides, Notes on Probability)
• Non-Parametric Models
• Ensemble Methods

2. Unsupervised Learning

• Clustering
• More Clustering
• Outlier Detection
• Finding Similar Items

3. Linear Models

• Least Squares (Notes on Calculus, Notes on Linear Algebra, Notes on Linear/Quadratic Gradients)
• Nonlinear Regression
• Gradient Descent
• Robust Regression
• Feature Selection
• Regularization
• More Regularization
• Linear Classifiers
• More Linear Classifiers
• Feature Engineering
• Convolutions
• Kernel Methods
• Stochastic Gradient
• Boosting
• MLE and MAP (Notes on Max and Argmax)

4. Latent-Factor Models

• Principal Component Analysis
• More PCA
• Sparse Matrix Factorization
• Recommender Systems
• Nonlinear Dimensionality Reduction

5. Deep Learning

• Deep Learning
• More Deep Learning
• Convolutional Neural Networks
• More CNNs

Part 2: Data Science 573 and 575

• Structure Learning
• Sequence Mining
• Tensor Basics
• Semi-Supervised Learning
• PageRank
• Markov Chains and Monte Carlo

Part 3: Computer Science 540

A. Fundamentals

• 340 Overview
• Fundamentals of Learning
• Convexity (Notes on Norms)
• How Much Data?

B. Density Estimation

• Structured Prediction Motivation
• Density Estimation
• Multivariate Gaussians
• Mixture Models
• Generative Classifiers
• Expectation Maximization (Notes on EM)
• Kernel Density Estimation
• Probabilistic PCA, Factor Analysis, Independent Component Analysis

C. Graphical Models

• Markov Chains
• Monte Carlo Methods
• Message Passing
• Hidden Markov Models
• DAG Models
• More DAGs
• Undirected Graphical Models
• Approximate Inference
• Log-Linear Models
• Boltzmann Machines
• Conditional Random Fields
• Structured SVMs

D. Bayesian Learning

• Bayesian Statistics
• Empirical Bayes
• Conjguate Prios
• Hierarchical Bayes
• Topics Models
• Rejection/Importance Sampling
• Metropolis-Hastings
• Variational Inference
• Non-Parametric Bayes
• Infinite Mixture Models

E. More Deep Learning

• Neural Networks
• Double Descent Curves
• Deep Structured Models
• Fully-Convolutional Networks
• Recurrent Neural Networks
• Long Short Term Memory
• Faster Algorithms for Deep Learning?
• VAEs and GANs
• Attention

Part 4: Large-Scale Machine Learning

• Convex Optimization (Notes on Norms)
• Gradient Descent Progress (Notes on Convexity Inequalities, Notes on Implementing Gradient Descent)
• Gradient Descent Convergence
• Linear and Superlinear Convergence
• Subgradient Methods
• Projected-Gradient
• Proximal-Gradient
• Structured Regularization
• Coordinate Optimization
• Mirror Descent and Multi-Level Methods
• Randomized Algorithms
• Stochastic Subgradient
• Variance-Reduced Stochastic Gradient
• Kernel Methods and Fenchel Duality
• Online Learning
• Over-Parameterized Models

Part 5: Machine Learning Reading Group

• Parallel and Distributed Machine Learning
• Online, Active, and Causal Learning
• Reinforcement Learning
• Overview of Other Large/Notable Topics

Lecture Notes on Data Analysis, Statistics, and Machine Learning (Leland Wilkinson)

• Introduction
• Data
• Visualizing
• Exploring
• Summarizing
• Distributions
• Inference
• Predicting
• Smoothing
• Time Series
• Comparing
• Grouping
• Reducing
• Learning
• Anomalies
• Analyzing

Lecture Notes (Arun Debray)

• Algebraic Geometry

• Algebraic Geometry

• Applications of QFT to Geometry 

• Bridgeland Stability 

• Differential Topology

• Equivariant Homotopy Theory 

• Geometric Langlands 

• Index Theory 

• K-Theory

• Mathematical Gauge Theory 

• Mathematical Physics 

• Mirror Symmetry 

• Measure Theory 

• Methods of Applied Mathematics 

• Morse Theory 

• Quantum Complexity Theory 

• Quantum symmetries

• Rational Homotopy Theory 

• Representation Theory 

• Riemann Surfaces 

• Riemannian Geometry 

• Seiberg-Witten Theory and Four-Manifold Topology 

• Spin Geometry 

• Topological and geometric methods in QFT

• Topological phases of matter 

• Topological quantum field theory

• Summer 2016 

• Fall 2016 

• Geometric Langlands Seminar

• Geometric Satake Seminar

• Spring 2018

• Summer 2018

• Fall 2018

• Spring 2019

• Fall 2019

• Gromov-Witten Theory Seminar

• Summer 2016 

• Fall 2016

• Spring 2017

• Fall 2017

• Fall 2018 

• Spring 2019

• Fall 2020

• PCMI Preparatory Seminar

• Quantum Topology and Categorification Seminar

• The comparison of two cohomology operations

• Fun with 𝓔(1)-modules and pin^c bordism

• 2-local string bordism and the Adams spectral sequence

• Bordism and invertible field theories

• Topological phases and topological field theories

• Lattice models and TQFTs

• An Introduction to Cohomology

• The Card Game SET and Its Mathematics

• X_Y for Automata

• CS 109

• CS 109L

• CS 154

• CS 155

• CS 161

• CS 229

• CS 240H

• CS 255

• CS 355

• CS 468

• CME 193

• History 154

• Math 53

• Math 108

• Math 116

• Math 120

• Math 121

• Math 122

• Math 137

• Math 144

• Math 145

• Math 159

• Math 171

• Math 205A

• Math 210A

• Math 210C

• Math 215B

• Math 215C

• Math 217A

• Math 217C

• Physics 40 series

The collected works of F. W. Lawvere

Title	Year
The Category of Probabilistic Mappings – With Applications to Stochastic Processes, Statistics, and Pattern Recognition	1962
Functorial Semantics of Algebraic Theories (short notice)	1963
The group ring of a small category (abstract)	1963
An Elementary Theory of the Category of Sets (cf. 2005 long version with commentary)	1964
Algebraic Theories, Algebraic Categories, and Algebraic Functors	1965
Functorial Semantics of Elementary Theories (abstract)	1966
The Category of Categories as a Foundation for Mathematics	1966
Theories as Categories and the Completeness Theorem (abstract)	1967
Some Algebraic Problems in the Context of Functorial Semantic of Algebraic Theories	1968
Ordinal Sums and Equational Doctrines	1969
Diagonal Arguments and Cartesian Closed Categories	1969
Adjointness in Foundations	1969
Equality in Hyperdoctrines and Comprehension Schema as an Adjoint Functor	1970
Quantifiers and Sheaves	1970
Introduction to "Toposes, Algebraic Geometry and Logic"	1971
Theory of Categories over a Base Topos	1972
Metric Spaces, Generalized Logic, and Closed Categories	1973
Introduction to "Model Theory & Topoi"	1975
Variable Sets Etendu and Variable Structure in Topoi	1975
Continuously Variable Sets – Algebraic Geometry=Geometric Logic	1975
Variable Quantities and Variable Structures in Topoi	1976
Categorical Dynamics	1978
Toward the Description in a Smooth Topos of the Dynamically Possible Motions and Deformations of a Continuous Body	1980
Introduction to "Categories in Continuum Physics"	1982
Functorial Remarks on the General Concept of Chaos	1984
State Categories Closed Categories and the Existence of Semi-continuous Entropy Functions	1984
State Categories and Response Functors	1986
Categories of Spaces may not be Generalized Spaces as Exemplified by Directed Graphs	1986
Taking Categories Seriously	1986
On the Complete Lattice of Essential Localizations (with G.M. Kelly)	1988
Display of Graphics and their Applications Exemplifed by 2 Categories and the Hegelian Taco	1989
Qualitative Distinctions Between Some Toposes of Generalized Graphs	1989
Intrinsic Co-Heyting Boundaries and the Leibniz Rule in Certain Toposes	1991
More on Graphic Toposes	1991
Some Thoughts on the Future of Category Theory	1991
Categories of Space and Quantity	1992
Cohesive Toposes and Cantor's Lauter Einsen	1994
Tools for the Advancement of Objective Logic Closed Categories and Toposes	1994
Adjoints in and among Bicategories	1996
Grassmann's Dialectics and Category Theory	1996
Unity and Identity of Opposites in Calculus and Physics	1996
Toposes of Laws of Motion	1997
Volterra's Functionals and Covariant Cohesion of Space	1997
Outline of Synthetic Differential Geometry	1998
Are Homotopy Types the Same As Infinitesimal Skeleta? (abstract)	1998
Kinship and Mathematical Categories	1999
Comments on the Development of Topos Theory	2000
The Role of Cartesian Closed Categories in Foundations (Interview with Felice Cardone)	2000
Categorical Algebra for Continuum Micro Physics	2001
On the Duality Between Varietes and Algebraic Theories (with J. Adámek & J. Rosický)	2003
How Algebraic is Algebra? (with J. Adámek & J. Rosický)	2001
Linearization of Graphic Toposes via Coxeter Groups	2002
Foundations and Applications – Axiomatization and Education	2003
Continuous Categories Revisited (with J. Adámek & J. Rosický)	2003
Functorial Semantics of Algebraic Theories and Some Algebraic Problems in the Context of Functorial Semantics of Algebraic Theories	2004
Functorial Concepts of Complexity for Finite Automata	2004
Left and Right Adjoint Operations on Spaces and Data Types	2004
An Elementary Theory of the Category of Sets (long version) with commentary	2005
Grassmann Book Reviews	2005
John Isbell's Adequate Subcategories	2006
Axiomatic Cohesion	2007
Cohesive toposes: combinatorial and infinitesimal cases (lecture notes)	2008
Core Varieties Extensivity and Rig Geometry	2008
Interview with Maria Manuel Clementino and Jorge Picado	2008
Foreword To "Algebraic Theories"	2009
Open Problems in Topos Theory	2009
The Hopf Algebra of Möbius Intervals (with M. Menni)	2010
Categorical Dynamics (abstract)	2011
Euler's Continuum Functorially Vindicated	2011
What are Foundations of Geometry and Algebra? (abstract) (transcript)	2013
Internal Choice Holds in the Discrete Part of any Cohesive Topos Satisfying Stable Connected Codiscreteness (with M. Menni)	2015
Alexander Grothendieck and the Concept of Space	2015
Birkhoff's Theorem from a Geometric Perspective: A Simple Example	2016
Everyday physics of extended bodies or why functionals need analyzing	2017