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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.
ProbMod01linearregression
is due 20201111.ProbMod02factorgraph
is due 20210113.ProbMod03meanfield
is due 20210127.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. 

20201008Linear Regression  
Introduction  intro  
Linear Regression  PRML 1.1  PRML01  
20201015Probability Theory  
Probability Theory: Random Variables  8 5:13  PRML01  +  
Exercises:


Probability Theory: Probability Densities  10 7:21  PRML 1.2.1  PRML01  + 
Exercises:


Probability Theory: Expectations  12 9:24  PRML 1.2.2  PRML01  + 
Exercises:


Probability Theory: Covariance  14 14:19  PRML 1.2.2  PRML01  + 
Exercises:


20201022ML and MAP Estimation; Probabilistic Regression  
Probability Theory: Normal Distribution  24 11:07  PRML 1.2.4  PRML01  + 
Exercises:


Probability Theory: MaximumLikelihood Estimation of the Paramters of a Normal Distribution  26 17:39  PRML 1.2.5  PRML01  + 
Exercises:


Probability Theory: ML Estimate of Variance is Biased  28 4:38  PRML 1.2.4  PRML01  + 
Exercises:


Regression: MaximumLikelihood Parameter Estimation  30 14:39  PRML 1.2.5  PRML01  + 
Exercises:


Regression: Maximum A Posteriori Parameter Estimation  32 12:13  PRML 1.2.5  PRML01  + 
Exercises:


20201029Probabilistic Prediction; Classification  
Regression: Bayesian Prediction  34 13:11  PRML 1.2.6  PRML01  + 
Exercises:


Probabilistic Prediction: ML and MAP Parameter Estimation; Bayesian Prediction  36 8:06  PRML 1.2.6  PRML01  + 
Exercises:


Classification: ML and MAP  40 11:24  PRML 1.5–1.5.1; 1.5.3  PRML01  + 
Exercises:


Classification: Generative and Discriminative Models; Reject Option  42 7:48  PRML 1.5.3–1.5.4  PRML01  + 
Exercises:


20201105Classification: Loss; Disciminative Models  
Classification: Loss Matrices, Loss Functions  44 12:57  PRML 1.5.2  PRML01  + 
Exercises:


Classification: HighLevel Comparison of Probabilistic and NonProbabilistic Paradigms  46 4:16  PRML01  +  
Exercises:


Discriminative Models for Classification: Linear Discriminants  50 10:33  PRML 4.2–4.2.1  PRML04  + 
Exercises:


Discriminative Models for Classification: Nonlinear Discriminants; the Exponential Family  52 4:16  PRML 4.2.1  PRML04  + 
Exercises:


Discriminative Models for Classification: Basis Functions  54 9:25  PRML 4.3.1, 4.3.2  PRML04  
Discriminative Models for Classification: MaximumLikelihood  56 11:58  PRML 4.3.2  PRML04  + 
Exercises:


Discriminative Models for Classification: Logistic Regression for 2 Classes  58 13:28  PRML 4.3.3  PRML04  + 
Exercises:


Discriminative Models for Classification: Logistic Regression for K Classes  60 5:18  PRML 4.3.4  PRML04  + 
Exercises:


20201112Bayesian Networks: Factorization; Conditional Independence  
Bayesian Networks: Factorization and Corresponding Directed Acyclic Graph  100 11:00  PRML 8.1 (Intro)  PRML08  + 
Exercises:


Bayesian Networks: Plate Notation; Modeling Regression  102 10:28  PRML 8.1.1  PRML08  + 
Exercises:


Bayesian Networks: Counting Model Parameters  104 9:05  PRML 8.1.3  +  
Exercises:


Bayesian Networks: Conditional Independence  106 4:49  PRML 8.2 (Intro)  PRML08  + 
Exercises:


Bayesian Networks: Conditional Independence: TailtoTail and HeadtoTail Connections  108 10:21  PRML 8.2.1  
Bayesian Networks: Conditional Independence: HeadtoHead Connection  110 6:29  PRML 8.2.1  PRML08  + 
Exercises:


Bayesian Networks: Fuel Gauge Example; Explaining Away  PRML 8.2.1  +  
Exercises:


20201119BN: dSeparation and Markov Blanket; MRF: Conditional Indpendence Properties  
Bayesian Networks: dSeparation  114 9:55  PRML 8.2.2  PRML08  + 
Exercises:


Bayesian Networks: dSeparation examples: IID variables; Naive Bayes classifier  116 14:35  PRML 8.2.2  PRML08  
Bayesian Networks: Metaphor of filtering by factorization or by dseparation  118 2:59  PRML 8.2.2  +  
Exercises:


Bayesian Networks: Markov Blanket  120 5:55  PRML 8.2.2  +  
Exercises:


Markov Random Fields: Conditional Independence Properties  PRML 8.3.1  +  
Exercises:


20201126MRF: Factorization; Relations to BN; Inference on Chains and Trees  
Markov Random Fields: Factorization Properties  PRML 8.3.2, 8.3.3  +  
Exercises:


Graphical Models: Relations Between Directed and Undirected Models  PRML 8.3.4  +  
Exercises:


Markov Chain: Marginalization Using Distributive Law  128 9:58  PRML 8.4.1  
Markov Chain: Marginalization Using Distributive Law: Example  130 8:16  PRML08  +  
Exercises:


Markov Chain: Marginalization Using Message Passing  132 5:08  PRML 8.4.1  +  
Exercises:


Markov Chain: Marginalization Using Message Passing: Example  134 5:57  PRML08  
Markov Chain: Marginalization For All Variables  136 2:58  PRML 8.4.1  
MRF Trees  PRML 8.4.2  +  
Exercises:


20201203Factor Graphs; SumProduct Algorithm  
Factor Graphs  PRML 8.4.3  +  
Exercises:


SumProduct Algorithm  PRML 8.4.4  PRML08 Sect. 5, 6, 9.3  +  
Exercises:


20201210MaxProduct Algorithm; General Graphs; Loopy Belief Propagation  
MaxSum Algorithm  PRML 8.4.5  PRML08 Sect. 7, 8, 9.4  +  
Exercises:


Exact Inference in General Graphical Models  PRML 8.4.6  
Loopy Belief Propagation  PRML 8.4.7  
20201217Kernel 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: NearestNeighbor Estimator  408 5:53  DE 2.5  KDE Sect. 3.3, 3.4  
Kernel Density Estimation: VariableKernel 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  
20210107Hidden Markov Models  
Hidden Markov Models  HMM  +  
Exercises:


20210114Variational Inference  
Variational Inference  variationalinference  +  
Exercises:

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The preparation material for 20201015 has been finalized.
The preparation material for 20201022 has been finalized.
The preparation material for 20201029 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 20201105 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 metaparameter $\alpha$.
The preparation material for 20201112 has been finalized.
Assignment 1 has been graded and feedback released.
The preparation material for 20201119 has been finalized.
The preparation material for 20201126 has been finalized.
The preparation material for 20201203 has been finalized.
The preparation material for 20201210 has been finalized.
The preparation material for 20201217 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.