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See the UIBK course catalog entries for the lecture and the seminar for essential information. See also the companion course Probabilistic Models and Inference (VO1 + PS1, Master).
This course (VO+PS) can be allocated for FLAT (VO+PS) or Compiler Construction (VO+PS). If and only if this is what you intend, then register for Modern Aspects in Computer Science: Data Science (VO+PS) instead.
The course content depends on the audience:
The course takes place online. Find more information in OLAT on the VO and the PS.
The first one or two sessions will take place like a regular lecture, just online. Then, to maximize the benefits of online teaching we will proceed as follows:
Students are strongly encouraged to form study groups who work together (possibly online according to the regulations) for selfstudy and on ungraded exercises.
There is an online forum for students to ask and answer each other's questions, where also the instructors will answer questions.
The course will be based on the following texts, all freely available online:
A very accessible textbook on many of the topics covered in this and the companion course is Machine Learning: A Probabilistic Perspective by Kevin Murphy, MIT Press 2012.
The topics to be covered are negotiable. If you would like specific topics covered, please contact the instructor.
Videos are in OLAT.
Where “HTML; PDF” are available, the HTML slides are exactly what you see in class; you should download the PDF notes because they contain additional information. The exercises included in the notes are useful to consolidate the material presented in class; many of them are done in class.
In addition to the resources given below, this course extensively uses Bishop's slides for PRML chapter 1.
Any entries pertaining to the future should be considered indicative and may change any time without notice.
Date  Mandatory Preparation  Topic  Lecture Notes 

20201027  RL 1.11.6, 3.13.5; Videos 200, 202 (video names updated, content is unchanged)  Reinforcement Learning: Introduction, Markov Decision Process  RL Notes; RL Day 1 Slides 
20201103  RL 3.63.8, 4.14.4; Videos 210, 212, 214, 216, 218  Reinforcement Learning: Bellman Equations, Dynamic Programming  RL Notes; RL Day 2 Slides; RL Day 2 Workbook 
20201110  RL 5.1, 6.1,6.2,6.4,6.5, 9.1,9.2,9.3,9.4,9.5, 10.1, 11.1,11.3; Videos 220, 222, 224, 226, 228, 229  Reinforcement Learning: Modelfree Prediction and Control, Value Function Approximation  RL Notes; RL Day 3 Slides 
20201117  RL 13.113.5,13.7,13.8; Videos 230, 232, 234, 236  Reinforcement Learning: Policy Gradients and Actor Critic methods  RL Notes; RL Day 4 Slides 
Topic  Video  Text  Notes  Ex. 

20201006Linear Regression  
Introduction  intro  
Linear Regression  PRML 1.1  PRML01  
20201013Linear Regression: Regularization, Solving; Cross Validation  
Regression: Overfitting and Regularization  2 35:09  PRML 1.1  PRML01  + 
Exercises:


Regression: Solving for a firstorder polynomial  4 14:22  PRML 1.1  PRML01  + 
Exercises:


Model Selection: CrossValidation  6 9:13  PRML 1.3  PRML01  + 
Exercises:


20201020Linear Regression: Basis Functions and Design Matrix; KMeans Clustering  
Regression: Basis Functions  16 17:33  PRML 3.1 (Intro)  PRML01  + 
Exercises:


Regression: Solving with Design Matrix  18 10:14  PRML 3.1.1  PRML01  + 
Exercises:


Regression: Solving Regularized Regression with Design Matrix  20 8:12  PRML 3.1.4  PRML01  + 
Exercises:


Clustering: KMeans  22 22:48  PRML 9.1  kmeans  + 
Exercises:


20201124Neural Networks: Intro; Forward Propagation; Squared Error Function  
Neural Networks: Preface  300 8:42  nn  
Neural Networks: Model Neuron and Activation Functions  302 5:44  nn  
Neural Networks: Terminology and Notation  304 8:51  nn  
Neural Networks: Forward Propagation  306 9:19  nn  +  
Exercises:


Neural Networks: Remarks  308 7:37  nn  
Neural Networks: Universal Approximation Property  310 6:18  PRML Fig. 5.3  nn  
Gradient Descent  312 6:45  gradientdescent  +  
Exercises:


Neural Networks: Squared Error Function  314 08:01  nn  
20201201Neural Networks: GradientDescent Training by Error Backpropagation  
Chain Rule for Simple and Partial Derivatives  316 5:29  nn  +  
Exercises:


Neural Networks: Error Backpropagation via the Chain Rule  318 8:37  nn  +  
Exercises:


Neural Networks: Computing the Deltas Layer by Layer  320 6:38  nn  +  
Exercises:


Neural Networks: The Batch Gradient Descent Training Algorithm  322 5:39  nn  +  
Exercises:


Neural Networks: Variants of GradientDescent Training Methods  324 9:38  nn  
20201215Neural Netwoks: Cost Functions; Implementation Tricks; Convolution  
Neural Networks: Cost Functions and Output Units  326 17:45  PRML 5.2 (Intro); DL 6.2  nn Sect. 4  + 
Exercises:


Neural Networks: Getting Training to Perform Well  328 14:39  DL 8.7  nn Sect. 5  
Neural Networks: Smoothing (to Motivate Convolution)  330 10:17  nn Sect. 6  
Neural Networks: Convolution  332 12:53  nn Sect. 6  
Convolutional Neural Networks: Introduction  334 12:10  DL 9.2  nn Sect. 7  
Convolutional Neural Networks: Forward Propagation  336 17:08  nn Sect. 7.7  
Convolutional Neural Networks: Demonstration  338 7:17  nn Sect. 7.7, 7.8  +  
Exercises:


20210112Convolutional and Autoencoder Networks  
Convolutional Neural Networks: Pooling; Typical Architecture  340 6:05  nn Sect. 7.9–7.12  
Convolutional Neural Networks: Example Architecture and Features  342 9:05  nn Sect. 7.13, 7.14  
Convolutional Neural Networks: Error Backpropagation  344 9:24  nn Sect. 7.15, 7.16  +  
Exercises:


Convolutional Neural Networks: Variants of Connectivity  346 6:07  nn Sect. 8  
Neural Networks: Sparse Autoencoder; KullbackLeibler Divergence  350 12:18  nn Sect. 9  +  
Exercises:


Neural Networks: Features learned by a sparse autoencoder  352 6:08  nn Sect. 9.5  +  
Exercises:


20210119Kernel Methods  
Kernel Methods  PRML 6–6.2  PRML06 Sect. 2, 3, 5.1  +  
Exercises:

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Please read the given sections in the book, try to follow the math, and give the exercises referred to in the course notes a shot. We will look at these on Tuesday.