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#Computational Learning Theory

Showing: 46 courses
Simons Institute

Educability - Understanding Higher-Level Intelligence

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Exploring human educability: synthesizing learning from experience, others, and chaining knowledge. Proposes a computational approach to understanding and replicating broader human capabilities beyond example-based learning.
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18 Lesons
54 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Learning-Augmented Online Optimization

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Explore learning-augmented online optimization with Google's Ravi Kumar, covering computational and statistical gaps in learning and optimization techniques.
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1 Lesons
49 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Occam's Razor and Consistent Learners - Lecture 14

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Delve into advanced concepts of Occam's razor in machine learning, exploring consistent learner applications and theoretical foundations for model complexity.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

PAC Learning in Machine Learning Theory - Lecture 13

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Delve into computational learning theory, exploring the PAC model and Occam's razor theorem for advanced understanding of machine learning fundamentals and theoretical frameworks.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Proper vs Improper Quantum PAC Learning

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Explore quantum PAC learning theory through a comparative analysis of proper and improper learning methods, focusing on the Quantum Coupon Collector problem and its padded variant.
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1 Lesons
20 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Understanding VC Dimensions and Computational Learning Theory - Lecture 18

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Delve into advanced computational learning theory concepts and VC dimensions, exploring fundamental principles that shape machine learning algorithm capabilities and limitations.
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1 Lesons
1 hour 4 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Shattering and VC Dimension - Lecture 17B

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Delve into computational learning theory, focusing on shattering concepts and VC dimension fundamentals in machine learning through theoretical frameworks and practical applications.
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1 Lesons
30 minutes
On-Demand
Free-Video
UofU Data Science

Agnostic Learning in Computational Learning Theory - Lecture 17

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Delve into computational learning theory and explore advanced concepts of agnostic learning, focusing on theoretical frameworks and practical applications in machine learning.
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1 Lesons
51 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Positive and Negative Learnability Results - Lecture 16

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Explore key theoretical foundations of machine learning through positive and negative learnability results, enhancing your understanding of learning theory fundamentals.
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1 Lesons
53 minutes
On-Demand
Free-Video
UofU Data Science

Agnostic Learning in Computational Learning Theory - Lecture 16

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Dive into computational learning theory and explore the fundamental concepts of agnostic learning in machine learning applications.
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1 Lesons
27 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

PAC Learning of Decision Trees

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Explore the theoretical foundations of PAC learning algorithms and their application to decision tree construction in this advanced computational complexity seminar.
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1 Lesons
2 hours
On-Demand
Free-Video
Kolmogorov-Seminar

PAC Learning: From Finite Families to CNF Formulas - Lecture on March 20, 2023

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Dive into PAC learning concepts, exploring finite families, conjunctions, k-CNFs, and NP-completeness in proper learning for 2-term CNF through advanced computational complexity theory.
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1 Lesons
2 hours 1 minute
On-Demand
Free-Video
Kolmogorov-Seminar

Introduction to Probably Approximately Correct (PAC) Learning

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Dive into computational complexity and PAC-learning concepts through an in-depth seminar discussion at the prestigious Kolmogorov seminar series on complexity theory.
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1 Lesons
1 hour 41 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

Computable PAC Learning - Part 2

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Dive into advanced concepts of computable PAC learning through an in-depth exploration of theoretical frameworks and computational complexity in machine learning theory.
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1 Lesons
1 hour 32 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

Computable PAC Learning - Part 1

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Dive into computational complexity and PAC learning theory through an in-depth exploration of foundational concepts, statistical learning frameworks, and algorithmic approaches.
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1 Lesons
1 hour 25 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

Vapnik-Chervonenkis Theorem and Statistical Learning Theory

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Dive into the fundamental Vapnik-Chervonenkis theorem, exploring how VC-dimension relates to machine learning generalization and error rates in hypothesis spaces.
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1 Lesons
2 hours 35 minutes
On-Demand
Free-Video
UofU Data Science

Mistake Bound Learning and the Halving Algorithm - Lecture 8

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Dive into mistake bound learning principles and explore the Halving algorithm's role in machine learning theory and practical applications.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

How Good is a Learning Algorithm? Quantifying the Learning Process - Lecture 7B

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Explore the quantitative aspects of machine learning by examining how many examples are needed to effectively discover and validate hypotheses.
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1 Lesons
27 minutes
On-Demand
Free-Video
UofU Data Science

Positive and Negative Learnability Results in Machine Learning - Lecture 14B

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Explore fundamental learnability concepts in machine learning through Occam's razor theorem applications and function class analysis.
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1 Lesons
50 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Occam's Razor for Consistent Learners - Lecture 13B

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Explore the theoretical foundations of Occam's razor theorem and its applications in consistent machine learning algorithms through mathematical analysis.
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1 Lesons
38 minutes
On-Demand
Free-Video
UofU Data Science

Introduction to Computational Learning Theory - Lecture 12

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Explore fundamental concepts of computational learning theory and theoretical frameworks that underpin machine learning algorithms and their effectiveness.
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1 Lesons
1 hour 8 minutes
On-Demand
Free-Video
UofU Data Science

PAC Learning in Machine Learning Theory - Lecture 13

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Explore the fundamental concepts of PAC (Probably Approximately Correct) learning theory and its applications in theoretical machine learning frameworks.
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1 Lesons
41 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Occam's Razor and Consistent Learners - Lecture 14

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Delve into the principles of Occam's razor and its application in consistent learning algorithms, exploring key theoretical foundations of machine learning complexity.
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1 Lesons
25 minutes
On-Demand
Free-Video
UofU Data Science

The VC Dimension in Machine Learning - Lecture 18

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Delve into the fundamental concept of VC dimension in machine learning theory, exploring its relationship with shattering and its implications for learning algorithms.
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1 Lesons
41 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Understanding Hypothesis Spaces Through Shattering - Lecture 17

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Delve into the concept of shattering and its application in analyzing infinite hypothesis spaces within machine learning theory and computational learning frameworks.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

Agnostic Learning in Machine Learning - Lecture 16

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Dive into computational learning theory and explore the fundamental concepts of agnostic learning in machine learning applications and theoretical frameworks.
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1 Lesons
1 hour 14 minutes
On-Demand
Free-Video
UofU Data Science

Perceptron Mistake Bound Theorem in Machine Learning - Lecture 10

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Dive into the mathematical foundations of Perceptron algorithms, exploring the theorem and proof behind mistake bounds in machine learning classification.
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1 Lesons
1 hour 23 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Representations and Learning in Online Systems - Lecture 9

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Explore how hypothesis representation choices impact online learning algorithms and their effectiveness in machine learning applications.
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1 Lesons
22 minutes
On-Demand
Free-Video
ACM SIGPLAN

Languages with Decidable Learning: A Meta-theorem

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Explore finite-aspect checkable languages and their role in decidable learning, connecting expression syntax tree evaluation to finite tree automata for new learning results.
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1 Lesons
18 minutes
On-Demand
Free-Video
ACM SIGPLAN

On Learning Polynomial Recursive Programs

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Explore P-finite automata, a generalization of weighted automata with polynomial transition weights. Learn about their polynomial-time learnability in Angluin's MAT exact learning model.
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1 Lesons
19 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

Leslie G. Valiant - Turing Award Lecture 2010

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Explore computational learning theory, machine learning, and brain modeling with Leslie G. Valiant, 2010 ACM Turing Award winner, as he discusses his groundbreaking research and insights.
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14 Lesons
1 hour 4 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Learning of Neural Networks with Quantum Computers and Learning of Quantum States with Graphical Models

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Explores quantum algorithms for neural network learning and quantum state learning using graphical models, showcasing advancements in computational efficiency and sample complexity for specific quantum states.
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1 Lesons
52 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension

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Explore smoothed analysis in machine learning, focusing on low-dimensional concepts and their applications. Gain insights into new algorithms for agnostic learning and margin-based approaches.
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1 Lesons
44 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

A Perceptron Trio - Computational vs Statistical Gaps in Learning and Optimization

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Explore perceptron algorithms in machine learning, focusing on computational and statistical aspects in optimization and learning theory.
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1 Lesons
51 minutes
On-Demand
Free-Video
Simons Institute

New Directions in Property Testing

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Explore recent advancements in property testing algorithms, including new problems and frameworks connected to machine learning theory and high-dimensional data analysis.
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1 Lesons
58 minutes
On-Demand
Free-Video
UofU Data Science

Online Learning and Mistake Bound Analysis - Lecture 6B

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Explore online learning algorithms and performance quantification through mistake bound methodology, focusing on mistake-driven learning approaches.
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4 Lesons
16 minutes
On-Demand
Free-Video
UofU Data Science

Positive and Negative Learnability Results in Machine Learning - Lecture 15

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Delve into fundamental learnability concepts in machine learning, exploring both positive and negative results that shape our understanding of learning algorithms and their limitations.
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1 Lesons
18 minutes
On-Demand
Free-Video
Institute for Advanced Study

Characterizations of PAC Learnability

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Explore key characterizations of PAC learnability in machine learning theory with insights from postdoctoral researcher Nataly Brukhim.
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1 Lesons
15 minutes
On-Demand
Free-Video
Simons Institute

Agnostic Proper Learning of Monotone Functions: Beyond the Black-Box Correction Barrier

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Explore efficient algorithms for agnostic, proper learning of monotone Boolean functions, overcoming previous limitations and achieving near-optimal runtime complexity.
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1 Lesons
24 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Computational Learning Theory and Batch Learning - Lecture 12

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Delve into formal models of learnability, focusing on batch learning concepts, mistake bound models, and PAC learning theory in machine learning applications.
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12 Lesons
48 minutes
On-Demand
Free-Video
NCCR SwissMAP

Learning Sparse Boolean Functions: Neural Networks Need a Hierarchical Degree Chain

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Explore neural networks' hierarchical degree chain for learning sparse Boolean functions. Gain insights from EPFL expert E. Abbé.
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1 Lesons
49 minutes
On-Demand
Free-Video
Stony Brook Mathematics

Learning Low-Degree Functions on the Discrete Hypercube

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Explore computational learning theory through random query models and polynomial inequalities, improving estimates for query complexity in reconstructing hypercube functions.
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1 Lesons
44 minutes
On-Demand
Free-Video
Simons Institute

A Theory of Multi-objective Machine Learning

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Explore multi-objective learning as a unifying paradigm for robustness, collaboration, and fairness in machine learning, with technical tools and empirical evidence.
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1 Lesons
1 hour 11 minutes
On-Demand
Free-Video
BIMSA

Educability: Defining and Simulating Human Capabilities - ICBS 2024

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Explore "educability" as a defining human capability, synthesizing learning from experience and others to explain humanity's achievements and guide future technology development.
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1 Lesons
1 hour 3 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

High-Dimensional Convexity: Testing, Learning, and Complexity

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Explore the fascinating parallels between high-dimensional convex sets and monotone Boolean functions, examining algorithmic structures and mathematical complexities in Gaussian space.
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1 Lesons
49 minutes
On-Demand
Free-Video
Simons Institute

Domain Adaptation - 20 Years of Theory Chasing Practice

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Explore the evolving relationship between theoretical frameworks and practical applications in domain adaptation learning, examining key insights, challenges, and the widening theory-practice gap.
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1 Lesons
37 minutes
On-Demand
Free-Video