CS计算机代考程序代写 DATA.ML.100 Introduction to Pattern Recognition and Machine Learning (5 cr) – Introduction to PR and ML

DATA.ML.100 Introduction to Pattern Recognition and Machine Learning (5 cr) – Introduction to PR and ML

DATA.ML.100 Introduction to Pattern

Recognition and Machine Learning (5 cr)

Introduction to PR and ML

Joni Kämäräinen

August 2021

Computing Sciences

Tampere University

1

History

Pattern Recognition and Machine Learning

Figure 1: King-Sun Fu

(1930-1985)

Figure 2: Marvin Minsky

(1927-2016)

2

Pattern Recognition and Machine Learning (cont.)

Figure 3: Top ML scientists (Google Scholar) 3

Machine Learning Software

Software 1.0 – Human writes the code

How would you implement function f (x) that sorts its input x (see

examples below)?

f ({8, 3, 7}) = {3, 7, 8}
f ({11, 10,−1}) = {−1, 10, 11}
f ({5, 4, 3, 3, 2, 1}) = {1, 2, 3, 3, 4, 5}

. (1)

4

Software 2.0 – Computer writes the code

How would you implement the following autonomous driving

functions f ?

(a) lane detection (b) vehicle detection

Figure 4: Software for autonomous cars. Code by Junsheng Fu who

studied at TAU for his MSc (Tech) and PhD degrees (code available in

his Github page) and now works for Zenseact, Sweden

5

Machine Learning Engineer

• Collects and annotates data

• Implements and tunes a suitable ML model

• Tests the model, collects more data if needed, re-trains and
fine-tunes the model

6

Recent results

Recent results

Natural language processing (NLP)

Text generationi (GPT-2)1

https://talktotransformer.com/

1Alec Radford et al. “Language Models are Unsupervised Multitask Learners”.

In: (2019)

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https://talktotransformer.com/

Text understanding (BERT)2

https://www.pragnakalp.com/demos/BERT-NLP-QnA-Demo/

2Jacob Devlin et al. “BERT: Pre-training of Deep Bidirectional Transformers

for Language Understanding”. In: arXiv preprint arXiv:1810.04805 (2018)

8

https://www.pragnakalp.com/demos/BERT-NLP-QnA-Demo/

Recent results

Sound and speech

Speech synthesis and understanding3

3Y. Jia et al. “Direct speech-to-speech translation with a

sequence-to-sequence model”. In: Interspeech. 2019

9

Recent results

Image and video

Image captioning4

4A. Karpathy and L. Fei-Fei. “Deep Visual-Semantic Alignments for

Generating Image Descriptions”. In: CVPR. 2015

10

Image colorization5

5R. Zhang, P. Isola, and A.A. Efros. “Colorful Image Colorization”. In: ECCV.

2016

11

Realistic image synthesis6

6
Q. Chen and V. Koltun. “Photographic Image Synthesis with Cascaded Refinement Networks”. In: ICCV. 2017

12

NVidia GauGAN

13

Animation using GANs7

7A. Pumarola et al. “GANimation: Anatomically-aware Facial Animation from

a Single Image”. In: Proceedings of the European Conference on Computer

Vision (ECCV). 2018

14

Recent results

Robotics

Imitation learning8

8W. Yang et al. “Neural Network Controller for Autonomous Pile Loading

Revised”. In: IEEE Int. Conf. on Robotics and Automation (ICRA). Xi’an,

China, 2021. url: http://arxiv.org/abs/2103.12379

15

http://arxiv.org/abs/2103.12379

Reinforcement learning9

9A. Dag et al. “Monolithic vs. Hybrid Controller for Multi-objective

Sim-to-Real Learning”. In: IEEE/RSJ International Conference on Intelligent

Robots and Systems (IROS). Prague, Czech Rep., 2021

16

Summary

• What is the difference between Software 1.0 and 2.0?

17

History
Machine Learning Software
Recent results
Natural language processing (NLP)
Sound and speech
Image and video
Robotics