Forskjeller
Her vises forskjeller mellom den valgte versjonen og den nåværende versjonen av dokumentet.
Begge sider forrige revisjonForrige revisjonNeste revisjon | Forrige revisjonSiste revisjonBegge sider neste revisjon | ||
emner:fordypning:ttk23 [2019/11/12 21:28] – [Guest lectures] scibilia | emner:fordypning:ttk23 [2020/10/24 14:39] – [Guest lectures] scibilia | ||
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Linje 3: | Linje 3: | ||
**Instructors: | **Instructors: | ||
- | **Updated Info:** The course starts | + | **Updated Info:** The first lecture will take place on Monday 5 October |
+ | |||
+ | **Prerequisites:** For the academic part, it's useful to have some past knowledge on how to train neural networks. | ||
===== Description ===== | ===== Description ===== | ||
Linje 12: | Linje 14: | ||
==== Theory lectures (by Anastasios Lekkas) ==== | ==== Theory lectures (by Anastasios Lekkas) ==== | ||
+ | The lecture plan below is from last year. For the 2020 lectures, it will be enhanced with additional algorithms and details regarding their implementation. | ||
- | **Lecture 1** **(09/10/19, 10:15-13:00, R52):** | + | **Lecture 1 (October 5, 2020, 12:15 - 14:00, S4 (Sentralbygg 1))** |
* Course introduction: | * Course introduction: | ||
* Autonomy in the scope of the course. An introduction to reinforcement learning and its connection with dynamic programming. | * Autonomy in the scope of the course. An introduction to reinforcement learning and its connection with dynamic programming. | ||
* Markov Decision Processes and problem formulation | * Markov Decision Processes and problem formulation | ||
- | **Lecture 2** **(16/10/19, 10:15-13:00, R52):** | + | **Lecture 2 (October 12, 2020, 12:15 - 14:00, S4 (Sentralbygg 1))** |
* Optimal policies in discrete environments (Part 1): Value iteration and policy iteration | * Optimal policies in discrete environments (Part 1): Value iteration and policy iteration | ||
* Optimal policies in discrete environments (Part 2): Q-learning and SARSA | * Optimal policies in discrete environments (Part 2): Q-learning and SARSA | ||
- | **Lecture 3** **(23/10/19, 10:15-13:00, R52):** | + | **Lecture 3 (October |
* Deep learning and deep reinforcement learning. | * Deep learning and deep reinforcement learning. | ||
* The DQN algorithm. | * The DQN algorithm. | ||
* Introduction to policy gradient algorithms for continuous action and state spaces. REINFORCE algorithm | * Introduction to policy gradient algorithms for continuous action and state spaces. REINFORCE algorithm | ||
- | **Lecture 4** **(30/10/19, 10:15-12:00, R52):** | + | **Lecture 4 (October 26, 2020, 12:15 - 14:00, S4 (Sentralbygg 1))** |
* Deep deterministic policy gradients (DDPG) | * Deep deterministic policy gradients (DDPG) | ||
- | * Model predictive control and reinforcement learning | + | * Proximal policy optimization |
==== Industry lectures (by Francesco Scibilia) ==== | ==== Industry lectures (by Francesco Scibilia) ==== | ||
- | **Lecture 5** **(06/11/19, 10:15-12:00, R52):** Artificial intelligence in autonomous robotics systems: what is an actionable definition in an industrial setting. Different levels of autonomy. Hierarchical architecture (Sense‐plan‐act and behaviorbased substrates) and autonomy layers. | + | **Lecture 5 (November 2, 2020, 12:15 - 14:00, S4 (Sentralbygg 1))** |
+ | |||
+ | Artificial intelligence in autonomous robotics systems: what is an actionable definition in an industrial setting. Different levels of autonomy. Hierarchical architecture (Sense‐plan‐act and behaviorbased substrates) and autonomy layers. | ||
- | **Lecture 6** **(13/11/19, 10:15-12:00, R52):** Autonomous mobile robotics systems – environment perception vs mission sensors, localization and mapping, navigation and sense & avoid. Considerations | + | **Lecture 6 (November 9, 2020, 12:15 - 14:00, TBD)** |
+ | |||
+ | AI Robotics, market value chain considerations. Operational considerations | ||
==== Guest lectures ==== | ==== Guest lectures ==== | ||
- | **Lecture 7** **(18/ | ||
- | Invited lecturer Steffan Sørenes, Equinor Leading Advisor Plant IT Architecture & Integration, | ||
- | Invited lecturer Edmund Henry Knutsen, Siemens O&G Product Lifecycle Manager for Digitalization, will talk about technologies | + | **Lecture 7 (November 6, 2020, 11:45 - 14:00, online(details to come) )** |
+ | |||
+ | * 11:45-12:00 Connecting | ||
+ | * 12:00-12:50 Guest lecture + QA – Steffan Sørenes, Leading Advisor IT Architecture at Equinor | ||
+ | * 12:50-13:10 Break | ||
+ | * 13:10-14:00 Guest lecture + QA – Fakhri Landolsi, Manager Data Science at Equinor | ||
- | Invited lecturer Nicolai Husteli, Scout Drone Inspection Chief Executive Officer, will talk about aerial robotics for autonomous visual inspection and non-destructive testing. | + | ==== Oral Exam ==== |
- | ==== Exam ==== | + | November |
- | **28 & 29 November** | + |