Case+study+2018

= Paper 3 – May/Nov 2018 = 

About paper 3
The paper is normally written on the same day as paper 2 and has a duration of ** 1 hour **, with a ** maximum ** ** mark **of** 30 **, counting for ** 20% ** of the ** total subject grade **. Every year, it is a based on a case study or scenario that changes. Here is the [|case study] for the May/November 2018 exams. The topic is ** Self Driving Cars ** and ** Machine Learning ** How to study paper 3: http://ib.compscihub.net/paper3

Please download our lesson materials to your own computer! http://pan.rdfz.cn:80/#/link/67792FE072EEF80505C04AD8440C69CF 有效期限截止：2017-12-16

Class assignment: Overview/ Survey of AI as a whole picture 20171122

Our paper 3 course are going to be in 4 stages:

Stage 1: study MIT course
Learn MIT 6.S094- Deep Learning and Self-Driving Cars Course! Course website: https://selfdrivingcars.mit.edu/ ,all slides are videos can be downloaded from this page There are 5 lectures, each about 90 minutes.

Lecture 1: learning summary deadline 20171120 Lecture 2: learning summary deadline 20171206 Lecture 3: learning summary Lecture 4: learning summary Lecture 5: learning summary

Stage 2: practical hands-on projects
During course learning, you will do the following 2 projects: Deep Traffic: Tutorial: https://selfdrivingcars.mit.edu/deeptraffic/ project page: https://selfdrivingcars.mit.edu/deeptrafficjs/ create and evaluate your own NN DeepTraffic: learning summary + project submission

DeepTesla: Tutorial: https://selfdrivingcars.mit.edu/deeptesla/ project page: https://selfdrivingcars.mit.edu/deepteslajs/ DeepTesla: learning summary + project submission

Stage 3: Baidu apollo auto driving platform
We are going to simulate our own self driving cars in baidu open source platform. You are going to have a contest for auto drive! http://apollo.auto/index_cn.html http://apollo.auto/developer_cn.html http://mp.weixin.qq.com/s/9pdQU1L0CJwu_pjEIIg--Q https://m.sohu.com/a/202555419_355140/?pvid=000115_3w_a

Stage 4: summarize all the key points in the case study material
To formulate your own case study booklet + webpage Then we are going to review all materials to develop an online high school self driving course by ourselves.

https://computersciencewiki.org/index.php/Template_for_student-defined_terms Template for students defined terms

__**challenges faced:**__


 * understand the basic functioning of CNNs as outlined in the case study (Tom)


 * analyse and test the nearest-neighbour and Dijkstra’s algorithms that have been considered for the bus and taxi projects (Arthur)


 * be able to respond to the social and ethical challenges to their project (Charles)


 * incorporate appropriate technology throughout the town that would support their autonomous vehicles project. (Alex)

__**social issues:**__


 * the “Trolley Problem”
 * the use of neural networks that produce solutions that we don’t really understand (Michael)


 * the beta-testing of autonomous car systems on public roads. (Doris)


 * understand the basic theory involved in the functioning of the path-fnding algorithms being employed. (Enzo)


 * 1) Autonomous (Doris)
 * 2) Backpropagation (Tom)
 * 3) BigO notation (Doris)
 * 4) Bounding boxes (Doris)
 * 5) Brute-force (Enzo)
 * 6) Convolutional neural networks (CNNs) (Tom)
 * 7) Cost function (Arthur)
 * 8) Deep learning (Tom)
 * 9) Dijkstra’s algorithm (Enzo)
 * 10) End-to-end learning (Tom)
 * 11) Feature maps (Activation maps) (Alex)
 * 12) Filters (Kernels) (Alex)
 * 13) Filter stride (Alex)
 * 14) Greedy algorithm (Arthur)
 * 15) Machine learning (Michael)
 * 16) Max-pooling / Pooling (Charles)
 * 17) Multi-layer perceptron (MLP) (Arthur)
 * 18) Nearest neighbour algorithm (Arthur)
 * 19) Overfitting (Charles)
 * 20) Point clouds （Doris）
 * 21) Receptive field (Alex)
 * 22) Sensor Fusion (Charles)
 * 23) Society of Automotive Engineers (Enzo)
 * 24) Shift invariance (Spatial invariance) (Michael)
 * 25) Vehicle-to-vehicle (VTV) protocol (Michael)
 * 26) Vehicle-to-infrastructure (VTI) protocol (Michael)