Artificial Intelligence (AI-2) SS 2021 /CoursesID:2095

Detailed information

Most recent entry on 2021-07-09 

Organisational Unit

Friedrich-Alexander-Universität Erlangen-Nürnberg

Recording type

Vorlesungsreihe

Via

Free

Language

English

The second semester of the general AI course at FAU, held Summer Semester 2021.

Course chapters

Episode
Title
Lecturer
Updated
Via
Duration
Media
1
Recap Clip 3.1: Sources of Uncertainty
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:02:20
2
Recap Clip 3.2: Recap: Rational Agents as a Conceptual Framework
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:01:34
3
Recap Clip 3.3: Agent Architectures based on Belief States
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:05:38
4
Recap Clip 3.4: Modeling Uncertainty
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:09:52
5
Recap Clip 3.5: Acting Under Uncertainty
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:38
6
Recap Clip 3.6: Agenda for this Chapter: Basics of Probability Theory
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:01:07
7
Recap Clip 3.7: Unconditional Probabilities (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:26
8
Recap Clip 3.8: Unconditional Probabilities (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:09:58
9
Recap Clip 3.9: Conditional Probabilities
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:01:21
10
Recap Clip 3.10: Independence
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:40
11
Recap Clip 3.11: Basic Probabilistic Reasoning Methods
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:12:07
12
Recap Clip 3.12: Bayes' Rule
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:02:26
13
Recap Clip 3.13: Conditional Independence
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:09:11
14
Recap Clip 4.1: Introduction
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:02:41
15
Recap Clip 4.2: What is a Bayesian Network?
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:12
16
Recap Clip 4.3: What is the Meaning of a Bayesian Network?
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:33
17
Recap Clip 4.4: Constructing Bayesian Networks (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:05:52
18
Recap Clip 4.5: Constructing Bayesian Networks (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:08:17
19
Recap Clip 4.6: Inference in Bayesian Networks
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:05:55
20
Recap Clip 4.7: Conclusion
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:04:57
21
Recap Clip 5.1: Introduction
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:05:08
22
Recap Clip 5.2: Rational Preferences
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:02:04
23
Recap Clip 5.3: Utilities and Money (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:51
24
Recap Clip 5.4: Utilities and Money (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:00
25
Recap Clip 5.5: Multi-Attribute Utility (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:03
26
Recap Clip 5.6: Multi-Attribute Utility (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:38
27
Recap Clip 5.7: Decision Networks
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:14:48
28
Recap Clip 6.?: Modeling Time and Uncertainty
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:11:11
29
Recap Clip 6.3: Inference: Filtering, Prediction and Smoothing (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:57
30
Recap Clip 6.4: Inference: Filtering, Prediction and Smoothing (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:32
31
Recap Clip 6.5: Inference: Filtering, Prediction and Smoothing (Part 3)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:06
32
Recap Clip 6.6: Hidden Markov Models (Part 1)
Dennis Müller
2021-03-30
Free
00:08:14
33
Recap Clip 6.7: Hidden Markov Models (Part 2)
Dennis Müller
2021-03-30
Free
00:10:39
34
Recap Clip 6.8: Dynamic Bayesian Networks
Dennis Müller
2021-03-30
Free
00:07:19
35
Recap Clip 7.1: Making Complex Decisions
Dennis Müller
2021-03-30
Free
00:01:23
36
Recap Clip 7.2: Sequential Decision Problems
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:07:07
37
Recap Clip 7.4: Value/Policy Iteration
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:04:42
38
Recap Clip 7.5: Partially Observable MDPs
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:05:16
39
Recap Clip 7.6: Online Agents with POMDPs
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:08:15
40
Recap Clip 8.2: Forms of Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:11:22
41
Recap Clip 8.3: Inductive Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:58
42
Recap Clip 8.4: Learning Decision Trees
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:04:35
43
Recap Clip 8.5: Using Information Theory (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:05:10
44
Recap Clip 8.6: Using Information Theory (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:07:23
45
Recap Clip 8.7: Evaluating and Choosing the Best Hypothesis (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:10
46
Recap Clip 8.8: Evaluating and Choosing the Best Hypothesis (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:09:40
47
Recap Clip 8.9: Computational Learning Theory (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:05:01
48
Recap Clip 8.10: Computational Learning Theory (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:04
49
Recap Clip 8.11: Regression and Classification with Linear Models (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:31
50
Recap Clip 8.12: Regression and Classification with Linear Models (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:02:30
51
Recap Clip 8.13: Regression and Classification with Linear Models (Part 3)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:04:55
52
Recap Clip 8.14: Artificial Neural Networks (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:09:49
53
Recap Clip 8.15: Artificial Neural Networks (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:12
54
Recap Clip 8.16: Artificial Neural Networks (Part 3)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:23
55
Recap Clip 8.17: Artificial Neural Networks (Part 4)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:08:24
56
Recap Clip 8.18: Support Vector Machines
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:12:16
57
Recap Clip 9.1: Full Bayesian Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:11:41
58
Recap Clip 10.1: Logical Formulations of Learning (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:07:52
59
Recap Clip 10.2: Logical Formulations of Learning (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:00:58
60
Recap Clip 10.3: Explanation-Based Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:26
61
Recap Clip 10.4: Relevance-Based Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:13:33
62
Recap Clip 10.5: Inductive Logic Programming: An Example
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:07:44
63
Recap Clip 10.7: Inductive Logic Programming: Inverse Resolution
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:06:56
64
Recap Clip 11.1: Reinforcement Learning: Introduction & Motivation
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:04:29
Episode
Title
Lecturer
Updated
Via
Duration
Media
3
Lecture1. Admin/Overview
Prof. Dr. Michael Kohlhase
2021-04-13
Free
01:31:38
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
20.1.1. Sources of Uncertainty
Prof. Dr. Michael Kohlhase
2021-01-11
Free
00:07:35
2
20.1.2. Recap: Rational Agents as a Conceptual Framework
Prof. Dr. Michael Kohlhase
2021-01-11
Free
00:14:27
3
20.1.3. Agent Architectures based on Belief States
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:14:08
4
20.1.4. Modeling Uncertainty
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:28:00
5
20.1.5. Acting Under Uncertainty
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:11:56
6
20.1.6. Agenda for this Chapter: Basics of Probability Theory
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:01:56
7
20.2. Unconditional Probabilities (Part 1)
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:17:31
8
20.2. Unconditional Probabilities (Part 2)
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:23:25
9
20.3. Conditional Probabilities
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:07:55
10
20.4. Independence
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:17:28
11
20.5. Basic Probabilistic Reasoning Methods
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:22:22
12
20.6. Bayes' Rule
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:13:52
13
20.7. Conditional Independence
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:32:10
14
20.8. The Wumpus World Revisited
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:14:18
15
20.9. Conclusion
Prof. Dr. Michael Kohlhase
2021-01-28
Free
00:01:46
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
21.1. Introduction
Prof. Dr. Michael Kohlhase
2021-02-01
Free
00:04:10
2
21.2. What is a Bayesian Network?
Prof. Dr. Michael Kohlhase
2021-02-01
Free
00:22:47
3
21.3. What is the Meaning of a Bayesian Network?
Prof. Dr. Michael Kohlhase
2021-02-01
Free
00:15:22
4
21.4. Constructing Bayesian Networks (Part 1)
Prof. Dr. Michael Kohlhase
2021-02-01
Free
00:20:13
5
21.4. Constructing Bayesian Networks (Part 2)
Prof. Dr. Michael Kohlhase
2021-02-01
Free
00:26:48
6
21.5. Inference in Bayesian Networks
Prof. Dr. Michael Kohlhase
2021-02-01
Free
00:25:50
7
21.6. Conclusion
Prof. Dr. Michael Kohlhase
2021-02-01
Free
00:07:58
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
22.1. Introduction
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:17:29
2
22.2 Rational Preferences
Prof. Dr. Michael Kohlhase
2021-05-09
Free
00:12:36
3
22.3. Utilities and Money (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:14:04
4
22.3. Utilities and Money (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:16:57
5
22.4. Multi-Attribute Utility (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:29:12
6
22.4. Multi-Attribute Utility (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:14:42
7
22.5. Decision Networks
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:24:07
8
22.6. The Value of Information (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:17:01
9
22.6. The Value of Information (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:18:20
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
23.1 Time and Uncertainty
Prof. Dr. Michael Kohlhase
2021-05-10
Free
00:27:22
2
23.2. Inference: Filtering, Prediction and Smoothing (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:29:04
3
23.2. Inference: Filtering, Prediction and Smoothing (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:23:41
4
23.2. Inference: Filtering, Prediction and Smoothing (Part 3)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:11:54
5
23.3. Hidden Markov Models (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:21:07
6
23.3. Hidden Markov Models (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:18:56
7
23.4. Dynamic Bayesian Networks
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:12:37
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
24. Making Complex Decisions
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:03:10
2
24.1. Sequential Decision Problems
Dennis Müller
2021-03-29
Free
00:11:56
3
24.2. Utilities over Time
Dennis Müller
2021-03-29
Free
00:21:54
4
24.3. Value/Policy Iteration
Dennis Müller
2021-03-29
Free
00:21:05
5
24.4. Partially Observable MDPs
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:20:42
6
24.5. Online Agents with POMDPs
Prof. Dr. Michael Kohlhase
2021-03-29
Free
00:21:57
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
25. Learning from Observations
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:03:49
2
25.1. Forms of Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:20:26
3
25.2. Inductive Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:21:43
4
25.3. Learning Decision Trees
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:17:33
5
25.4. Using Information Theory (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:27:33
6
25.4. Using Information Theory (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:26:53
7
25.5. Evaluating and Choosing the Best Hypothesis (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:22:00
8
25.5. Evaluating and Choosing the Best Hypothesis (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:31:59
9
25.6. Computational Learning Theory (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:25:36
10
25.6. Computational Learning Theory (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:15:22
11
25.7. Regression and Classification with Linear Models (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:17:46
12
25.7. Regression and Classification with Linear Models (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:18:21
13
25.7. Regression and Classification with Linear Models (Part 3)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:18:04
14
25.8. Artificial Neural Networks (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:19:21
15
25.8. Artificial Neural Networks (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:20:59
16
25.8. Artificial Neural Networks (Part 3)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:16:59
17
25.8. Artificial Neural Networks (Part 4)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:21:09
18
25.9. Support Vector Machines
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:28:28
19
Bonus: Science Slam
Jonas Betzendahl
2021-03-30
Free
00:12:41
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
26.1. Full Bayesian Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:13:26
2
26.2. Approximations of Bayesian Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:14:58
3
26.3. Parameter Learning for Bayesian Networks
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:17:25
4
26.4. Naive Bayes Models
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:11:19
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
27.1. Logical Formulations of Learning (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:20:00
2
27.1. Logical Formulations of Learning (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:21:38
3
27.2. Explanation-Based Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:32:40
4
27.3. Relevance-Based Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:26:56
5
27.4.1. Inductive Logic Programming: An Example
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:07:40
6
27.4.2. Inductive Logic Programming: Top-Down Inductive Learning: FOIL
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:18:48
7
27.4.3. Inductive Logic Programming: Inverse Resolution
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:21:22
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
28.1. Reinforcement Learning: Introduction & Motivation
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:11:14
2
28.2. Passive Learning
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:10:46
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
29.1 Introduction to NLP
Prof. Dr. Michael Kohlhase
2021-07-03
Free
00:16:16
2
29.2 Natural Language and its Meaning
Prof. Dr. Michael Kohlhase
2021-07-03
Free
00:24:57
3
29.3 Looking at Natural Language
Prof. Dr. Michael Kohlhase
2021-07-03
Free
00:28:16
4
29.4 Language Models
Prof. Dr. Michael Kohlhase
2021-07-01
Free
00:25:30
5
29.5 Information Retrieval
Prof. Dr. Michael Kohlhase
2021-07-02
Free
00:18:56
6
29.6 Word Embeddings
Prof. Dr. Michael Kohlhase
2021-07-02
Free
00:15:37
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
27.1 Communication Phenomena
Prof. Dr. Michael Kohlhase
2021-07-09
Free
00:13:57
2
30.2 Grammars and Syntactic Processing
Prof. Dr. Michael Kohlhase
2021-07-09
Free
00:45:55
3
30.3 Real Language Phenomena
Prof. Dr. Michael Kohlhase
2021-07-09
Free
00:14:39
Episode
Title
Lecturer
Updated
Via
Duration
Media
1
31. What did we learn in AI 1/2? (Part 1)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:27:11
2
31. What did we learn in AI 1/2? (Part 2)
Prof. Dr. Michael Kohlhase
2021-03-30
Free
00:19:14

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