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See the UIBK course catalog entries for the lecture and the seminar for essential information.
This course will be organized similarly to its companion course Advanced Machine Learning (VO2 + PS1, Master). The online sessions will last about one hour and will encompass VO discussion and the PS; thus, attendance is mandatory for all PS registrants.
There is an online forum for students to ask and answer each other's questions, where also the instructors will answer questions.
Suggest more and/or vote for any of these.
Programming assignments are to be downloaded from JupyterHub, solved on your local machine, and reuploaded to JupyterHub, exactly like in the AdvML PS.
ProbMod-01-linear-regression
is due 2020-11-11.ProbMod-02-factor-graph
is due 2021-01-13.ProbMod-03-mean-field
is due 2021-01-27.In addition to the resources given below, this course extensively uses Bishop's slides for PRML chapters 1 and 8.
The videos are in OLAT.
Topic | Video | Text | Notes | Ex. |
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2020-10-08Linear Regression | ||||
Introduction | intro | |||
Linear Regression | PRML 1.1 | PRML-01 | ||
2020-10-15Probability Theory | ||||
Probability Theory: Random Variables | 8 5:13 | PRML-01 | + | |
Exercises:
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Probability Theory: Probability Densities | 10 7:21 | PRML 1.2.1 | PRML-01 | + |
Exercises:
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Probability Theory: Expectations | 12 9:24 | PRML 1.2.2 | PRML-01 | + |
Exercises:
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Probability Theory: Covariance | 14 14:19 | PRML 1.2.2 | PRML-01 | + |
Exercises:
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2020-10-22ML and MAP Estimation; Probabilistic Regression | ||||
Probability Theory: Normal Distribution | 24 11:07 | PRML 1.2.4 | PRML-01 | + |
Exercises:
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Probability Theory: Maximum-Likelihood Estimation of the Paramters of a Normal Distribution | 26 17:39 | PRML 1.2.5 | PRML-01 | + |
Exercises:
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Probability Theory: ML Estimate of Variance is Biased | 28 4:38 | PRML 1.2.4 | PRML-01 | + |
Exercises:
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Regression: Maximum-Likelihood Parameter Estimation | 30 14:39 | PRML 1.2.5 | PRML-01 | + |
Exercises:
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Regression: Maximum A Posteriori Parameter Estimation | 32 12:13 | PRML 1.2.5 | PRML-01 | + |
Exercises:
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2020-10-29Probabilistic Prediction; Classification | ||||
Regression: Bayesian Prediction | 34 13:11 | PRML 1.2.6 | PRML-01 | + |
Exercises:
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Probabilistic Prediction: ML and MAP Parameter Estimation; Bayesian Prediction | 36 8:06 | PRML 1.2.6 | PRML-01 | + |
Exercises:
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Classification: ML and MAP | 40 11:24 | PRML 1.5–1.5.1; 1.5.3 | PRML-01 | + |
Exercises:
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Classification: Generative and Discriminative Models; Reject Option | 42 7:48 | PRML 1.5.3–1.5.4 | PRML-01 | + |
Exercises:
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2020-11-05Classification: Loss; Disciminative Models | ||||
Classification: Loss Matrices, Loss Functions | 44 12:57 | PRML 1.5.2 | PRML-01 | + |
Exercises:
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Classification: High-Level Comparison of Probabilistic and Non-Probabilistic Paradigms | 46 4:16 | PRML-01 | + | |
Exercises:
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Discriminative Models for Classification: Linear Discriminants | 50 10:33 | PRML 4.2–4.2.1 | PRML-04 | + |
Exercises:
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Discriminative Models for Classification: Nonlinear Discriminants; the Exponential Family | 52 4:16 | PRML 4.2.1 | PRML-04 | + |
Exercises:
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Discriminative Models for Classification: Basis Functions | 54 9:25 | PRML 4.3.1, 4.3.2 | PRML-04 | |
Discriminative Models for Classification: Maximum-Likelihood | 56 11:58 | PRML 4.3.2 | PRML-04 | + |
Exercises:
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Discriminative Models for Classification: Logistic Regression for 2 Classes | 58 13:28 | PRML 4.3.3 | PRML-04 | + |
Exercises:
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Discriminative Models for Classification: Logistic Regression for K Classes | 60 5:18 | PRML 4.3.4 | PRML-04 | + |
Exercises:
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2020-11-12Bayesian Networks: Factorization; Conditional Independence | ||||
Bayesian Networks: Factorization and Corresponding Directed Acyclic Graph | 100 11:00 | PRML 8.1 (Intro) | PRML-08 | + |
Exercises:
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Bayesian Networks: Plate Notation; Modeling Regression | 102 10:28 | PRML 8.1.1 | PRML-08 | + |
Exercises:
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Bayesian Networks: Counting Model Parameters | 104 9:05 | PRML 8.1.3 | + | |
Exercises:
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Bayesian Networks: Conditional Independence | 106 4:49 | PRML 8.2 (Intro) | PRML-08 | + |
Exercises:
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Bayesian Networks: Conditional Independence: Tail-to-Tail and Head-to-Tail Connections | 108 10:21 | PRML 8.2.1 | ||
Bayesian Networks: Conditional Independence: Head-to-Head Connection | 110 6:29 | PRML 8.2.1 | PRML-08 | + |
Exercises:
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Bayesian Networks: Fuel Gauge Example; Explaining Away | PRML 8.2.1 | + | ||
Exercises:
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2020-11-19BN: d-Separation and Markov Blanket; MRF: Conditional Indpendence Properties | ||||
Bayesian Networks: d-Separation | 114 9:55 | PRML 8.2.2 | PRML-08 | + |
Exercises:
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Bayesian Networks: d-Separation examples: IID variables; Naive Bayes classifier | 116 14:35 | PRML 8.2.2 | PRML-08 | |
Bayesian Networks: Metaphor of filtering by factorization or by d-separation | 118 2:59 | PRML 8.2.2 | + | |
Exercises:
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Bayesian Networks: Markov Blanket | 120 5:55 | PRML 8.2.2 | + | |
Exercises:
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Markov Random Fields: Conditional Independence Properties | PRML 8.3.1 | + | ||
Exercises:
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2020-11-26MRF: Factorization; Relations to BN; Inference on Chains and Trees | ||||
Markov Random Fields: Factorization Properties | PRML 8.3.2, 8.3.3 | + | ||
Exercises:
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Graphical Models: Relations Between Directed and Undirected Models | PRML 8.3.4 | + | ||
Exercises:
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Markov Chain: Marginalization Using Distributive Law | 128 9:58 | PRML 8.4.1 | ||
Markov Chain: Marginalization Using Distributive Law: Example | 130 8:16 | PRML-08 | + | |
Exercises:
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Markov Chain: Marginalization Using Message Passing | 132 5:08 | PRML 8.4.1 | + | |
Exercises:
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Markov Chain: Marginalization Using Message Passing: Example | 134 5:57 | PRML-08 | ||
Markov Chain: Marginalization For All Variables | 136 2:58 | PRML 8.4.1 | ||
MRF Trees | PRML 8.4.2 | + | ||
Exercises:
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2020-12-03Factor Graphs; Sum-Product Algorithm | ||||
Factor Graphs | PRML 8.4.3 | + | ||
Exercises:
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Sum-Product Algorithm | PRML 8.4.4 | PRML-08 Sect. 5, 6, 9.3 | + | |
Exercises:
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2020-12-10Max-Product Algorithm; General Graphs; Loopy Belief Propagation | ||||
Max-Sum Algorithm | PRML 8.4.5 | PRML-08 Sect. 7, 8, 9.4 | + | |
Exercises:
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Exact Inference in General Graphical Models | PRML 8.4.6 | |||
Loopy Belief Propagation | PRML 8.4.7 | |||
2020-12-17Kernel Density Estimation | ||||
Kernel Density Estimation: Motivation | 400 7:49 | DE 1 | KDE Sect. 2 | |
Kernel Density Estimation: Histogram | 402 8:01 | DE 2.2 | KDE Sect. 2.6 | |
Kernel Density Estimation: Naive Estimator | 404 6:56 | DE 2.3 | KDE Sect. 2.7, 2.8 | |
Kernel Density Estimation: Kernel Estimator | 406 3:45 | DE 2.4 | KDE Sect. 3.1, 3.2 | |
Kernel Density Estimation: Nearest-Neighbor Estimator | 408 5:53 | DE 2.5 | KDE Sect. 3.3, 3.4 | |
Kernel Density Estimation: Variable-Kernel Estimator; General Remarks | 410 4:52 | DE 2.6 | KDE Sect. 3.5, 4 | |
Kernel Density Estimation: Bounded Domains | 412 6:50 | DE 2.10 | KDE Sect. 5 | |
Kernel Density Estimation: Circular Domains | 414 4:04 | KDE Sect. 5 | ||
2021-01-07Hidden Markov Models | ||||
Hidden Markov Models | HMM | + | ||
Exercises:
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2021-01-14Variational Inference | ||||
Variational Inference | variational-inference | + | ||
Exercises:
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RSS Feed
The preparation material for 2020-10-15 has been finalized.
The preparation material for 2020-10-22 has been finalized.
The preparation material for 2020-10-29 has been finalized.
This page is now accessible from the internet; a VPN is no longer required.
The first programming assignment is out.
The preparation material for 2020-11-05 has been finalized.
Assignment 1 Task 3 Opening Paragraph
Write a function that computes the predictive distribution expressed by the vectors $\hat{\mu}$ and $\hat{\sigma}^2$ for a set of test input values, received in the form of its design matrix $\Phi_{\textrm{test}}$, given the design matrix $\Phi$ and target values $\mathbf{t}$ of the training data, and the meta-parameter $\alpha$.
The preparation material for 2020-11-12 has been finalized.
Assignment 1 has been graded and feedback released.
The preparation material for 2020-11-19 has been finalized.
The preparation material for 2020-11-26 has been finalized.
The preparation material for 2020-12-03 has been finalized.
The preparation material for 2020-12-10 has been finalized.
The preparation material for 2020-12-17 has been finalized.
The second programming assignment is out.
Oral exams will take place February 4 online. Please register by February 21.
Online exams are subject to the conditions Absolvierung der kommissionellen Abschlussprüfung bzw. Defensio. In addition:
The third (and last) programming assignment is out.