Содержание курса
16
разделов
204
урока
377
тестов
0 ч.
обн. 21 августа 2026
Introduction
11 уроков
1.
Information and meaning: why they are not the same thing
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2.
The Shannon frame: strength and limitations
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3.
Why logarithms appear: a short bridge to entropy
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4.
How the course is structured and how to read it
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5.
Course program: overview
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6.
Literature: where to start
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7.
Reference: detailed course program
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8.
Reference: complete annotated reading list
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9.
Optional mathematical deepening I: the uniform source
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Optional mathematical deepening II: from grouping to Shannon…
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11.
Computational block
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Entropy
8 уроков
1.
Entropy: the average bill for uncertainty
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2.
The chain rule: splitting one bill across tokens
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3.
From a coin to language: entropy rate
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4.
How to read entropy in a language model
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5.
Exercises
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6.
References and primary sources
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7.
Computational block
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8.
Exercise solutions
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Cross-Entropy and KL Divergence
13 уроков
1.
The model returns a distribution; the world returns one outcome
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2.
Cross-entropy: the model's average logarithmic bill
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3.
KL divergence: the part of the loss attributable to the model
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4.
The wrong code: how KL becomes extra bits
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One formula for classification and language modeling
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Why CrossEntropyLoss is so convenient to optimize
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7.
Perplexity: returning from the logarithmic scale
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8.
Mathematical deepening: why KL direction changes the answer
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9.
A map of KL objectives in machine learning
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Exercises
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11.
References and primary sources
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12.
Computational block
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13.
Exercise solutions
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Jensen's inequality: when averaging meets nonlinearity
11 уроков
1.
Average first, or evaluate the loss first?
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2.
Jensen’s inequality: curvature sets the direction
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3.
Ensembles: an exact guarantee for log loss
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4.
The ELBO: the logarithm sits outside hidden explanations
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5.
The log-sum inequality: what is lost when states are merged
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6.
One theorem, four working mechanisms
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7.
What to carry into ML practice
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8.
Exercises
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9.
References and primary sources
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10.
Computational block
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11.
Exercise solutions
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Mutual information: how much one object tells us about another
16 уроков
1.
A feature is useful only relative to a target
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2.
One quantity, three readings
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3.
PMI: the local log ratio of association
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Conditional MI: what a new feature adds beyond known features
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DPI: an encoder can repackage information, but it cannot…
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MI, correlation, and causality answer different questions
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Noisy labels: when 100% error still carries one bit
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8.
Feature selection: a useful filter, not an oracle
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InfoNCE: finding the matched pair among random candidates
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Why MI is hard to estimate from samples
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11.
Attention entropy is not mutual information
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12.
What to carry into ML practice
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13.
Exercises
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14.
Sources and further reading
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15.
Computational block
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Exercise solutions
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Source coding: from entropy to real bits
16 уроков
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Engineering setup: the probabilistic model and the entropy coder
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2.
Prefix codes and the Kraft--McMillan inequality
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The one-symbol source-coding theorem
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4.
Huffman coding, arithmetic coding, and range coding
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Long sequences: block coding, the AEP, and entropy rate
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Language-model metrics: PPL, BPC, and BPB
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A language model as part of a compressor
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Model size, scaling laws, and total description length
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Mathematical deepening: universal coding, sequential log…
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Compression quality and model capabilities
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A practical protocol for evaluating an LLM as a compressor
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12.
Exercises
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13.
Conclusion
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14.
Primary sources
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15.
Computational block
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Exercise solutions
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Channel and capacity
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1.
Reliable communication through noise
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2.
A discrete memoryless channel and a block code
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Capacity of a discrete channel
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BSC, BEC, and the Z-channel
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The Gaussian channel and a power constraint
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Why random coding achieves \(R<I(X;Y)\)
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Converse and finite blocklength
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Noisy labels as a channel
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Channel coding inside ML: ECOC and DeepJSCC
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An LLM as a channel: a precise model and the limits of the…
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Formalizing a channel in an ML system
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12.
Exercises
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13.
Conclusion
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Primary sources and further reading
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Computational block
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Exercise solutions
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Maximum entropy, exponential models, and KL regularization
15 уроков
1.
From partial knowledge to a distribution
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2.
Exponential tilting and information projection
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3.
The state space and reference measure
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Classical MaxEnt distributions and the AWGN channel
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Conditional MaxEnt and log-linear models
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Softmax as score-plus-entropy optimization
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Energy-based models and the partition function
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A KL-regularized policy and its Gibbs optimum
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Reward overoptimization and the role of KL
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Mathematical deepening: exponential families and sufficiency
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Exercises
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Conclusion
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Primary sources
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Computational block
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Exercise solutions
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Rate–distortion and the Information Bottleneck: managing…
14 уроков
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Why a model may need to forget
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Rate–distortion theory: how much information must be retained
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Information Bottleneck: preserve what matters for \(Y\)
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Variational Information Bottleneck: making IB trainable
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The VAE as a rate–distortion system
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When rate becomes actual bits
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Building an honest rate–distortion curve
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The information plane: an objective or a description of…
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Mathematical extension: I–MMSE and Gaussian noising
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Exercises
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What to carry forward
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Primary sources
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13.
Computational block
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14.
Exercise solutions
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Algorithmic Information Theory: Shortest Descriptions, MDL…
14 уроков
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From a distribution to one particular object
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Kolmogorov complexity and the description language
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Incompressibility, uncomputability, and real compressors
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The bridge to Shannon entropy
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Algorithmic probability and universal prediction
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MDL in machine learning
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Similarity through joint compression
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LLMs and universal prediction
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Mathematical deepening: AIXI
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Optional physical bridge: Landauer's principle
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Exercises
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What to retain
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Primary sources
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Exercise solutions
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Comparing distributions: f-divergences, variational critics…
14 уроков
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From two sample sets to a distribution-comparison problem
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f-divergences: one template, different penalties
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What f-divergences can see
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Classification as density-ratio estimation
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Variational form: making a discrepancy trainable
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GAN and f-GAN
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MINE and InfoNCE
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Integral probability metrics: Wasserstein distance and MMD
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Choosing a discrepancy in practice
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Mathematical deepening: general measures and exact duality
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Exercises
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Conclusion
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Primary references
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Exercise solutions
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Information-theoretic generalization bounds: PAC-Bayes and…
14 уроков
1.
Why the best training error is almost inevitably too good
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2.
From a fixed model to adaptive selection
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PAC-Bayes: a distribution before data and a distribution…
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PAC-Bayes-kl: a certificate for a realized sample
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From a certificate to a trainable distribution
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MI bounds: learning as a channel from sample to model
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The exact decomposition connecting PAC-Bayes and MI
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Connections to MDL, Information Bottleneck, and…
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Building a practical PAC-Bayes certificate
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Mathematical deepening: more local information costs
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Exercises
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12.
What to take away
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Main references
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Exercise solutions
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Reasoning in LLMs: computation, search, and information
15 уроков
1.
What changes when a model “thinks longer”
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2.
Amortization, scratchpads, and search
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3.
Why intermediate tokens can help
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New information or new computation?
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What can be measured along a trajectory?
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One long trajectory or many candidates?
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In-context learning: when context really brings evidence
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How reasoning models are trained
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How to evaluate a reasoning system
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What is proved, what is observed, and what remains a hypothesis
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11.
Mathematical deepening: stepwise information gain
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12.
Exercises
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13.
Conclusion: information is not computation
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14.
Primary sources
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15.
Exercise solutions
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Computer Vision Through Information Theory
13 уроков
1.
Why computer vision is a useful testbed for information theory
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2.
Learned image compression: entropy models and actual bitstreams
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3.
Distortion and perceptual quality
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4.
Diffusion models: Gaussian noising, denoising, and I–MMSE
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CLIP and contrastive image–text alignment
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Self-supervised visual representations: invariances and…
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Compressing a CV model: quantization, pruning, and distillation
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One CV pipeline, four different information budgets
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Mathematical deepening: two exact calculations
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Exercises
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11.
Conclusion
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12.
Primary references
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Exercise solutions
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Information Geometry: Fisher, Natural Gradient, and…
13 уроков
1.
What Does a “Small Step” Mean for a Probabilistic Model?
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2.
Fisher Information and the Local Form of KL
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3.
Natural Gradient: Steepest Descent Under Small Model Change
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4.
What “Curvature” Is Actually Used in a Deep Network?
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Policy Geometry: NPG, TRPO, PPO, and KL in RLHF
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6.
Wasserstein Geometry and Distribution Flows
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Exponential Families, Dual Coordinates, and Mirror Descent
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Practical Map: Choose the Object Before the Geometry
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Mathematical Deepening: α-Geometry and Exact Examples
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10.
Exercises
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11.
Conclusion
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12.
Main Sources
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13.
Exercise solutions
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Course Reference Sheet
1 урок
1.
Course Reference Sheet: Key Ideas and Formulas
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