Artificial Intelligence Documentation

Artificial Intelligence DocumentationDec 28, 2020
1 Course Mission 3
2 Course Target 5
3 Course Instructors 7
4 Teaching Assistants 9
5 Content 11 5.1 C01 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 5.2 C02 Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 5.3 C03 Machine Learning and Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 5.4 C04 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5.5 C05 Deep Learning for Natural Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.6 C06 Deep Learning for Life Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.7 C07 Deep Learning for Social Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 5.8 C08 Deep Learning for Management Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.9 C09 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
i
ii
• Multidiscplinary Artificial Intelligence
Contents 1
2 Contents
CHAPTER 1
Course Mission
– Artificial Intelligence + Science
– Life Science: Healthcare, Drug Discovery. . .
– Social Science: Social Network, Social Structure, Policy Making. . .
– Management Science: Planning, Scheduling, Optimization, Supply Chain, Organization. . .
3
CHAPTER 2
Course Target
– Machine Learning, Deep Learning
– Natural Science, Life Science, Social Science, Management Science etc.
– To understand what’s going on in these fields.
• Expose to real, latest researches of techniques applied in these sciences
– Lots of literature would be used and studied
– How to use techniques to solve the problems in these fields?
5
CHAPTER 3
Course Instructors
• Dr. Yong Tang - School of Computer Science and Engineering, School of Economics and Management, UESTC
7
CHAPTER 4
Teaching Assistants
• Fall 2020
• Fall 2019
9
CHAPTER 5
5.1.2 Homework
C01-1-1. Visit http://Deepmind.com and http://yitutech.com. Please take a look at what the two AI companies are doing. Please write one-page report on how to turn AI technologies into business.
5.1.3 Material
AI Chips: https://www.anandtech.com/show/16010/hot-chips-2020-live-blog-silicon-photonics-for-ai-600pm-pt
DeepMind: http://Deepmind.com
OpenAI: http://openai.com
5.1.4 Literature
[1] P. M. R. DeVries, F. Vi egas, M. Wattenberg, and B. J. Meade. Deep learning of aftershock patterns following large earthquakes. Nature, 560(7720):632–634, 2018.
[2] J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, W. He, F. Chen, N. Deng, S. Wu, Y. Wang, Y. Wu, Z. Yang, C. Ma, G. Li, W. Han, H. Li, H. Wu, R. Zhao, Y. Xie, and L. Shi. Towards artificial general intelligence with hybrid tianjic chip architecture. Nature, 572(7767):106–111, 2019.
Artificial Intelligence Documentation, Release latest
[3] Q. Zhou, P. Tang, S. Liu, J. Pan, Q. Yan, and S.-C. Zhang. Learning atoms for materials discovery. Proceedings of the National Academy of Sciences, 2018.
5.2 C02 Python
5.2.1 Exercise
C02-1-1. Generate 100 numbers randomly, use bubble sort algorithm to sort. Please print the sorted numbers, and the max, min, mean.
C02-1-2. Input N, and D, output all twin prime pairs less than N of distance D.
C02-2-1. Input the number of nodes N and edge probability p, use networkx to generate an ER network. Please visualize the network, save the picture as an EPS file. Calculate the average degree, average betweenness centrality, plot degree distribution and betweenness centrality distribution.
5.2.2 Homework
C02-1-1. Input an integer N and output all possible solutions of Goldbach conjecture. For example, N=10, the program should output: 4=2+2, 6=3+3, 8=3+5, 10=3+7, 10=5+5.
C02-2-1. Input the number of nodes N, the distance D, and node ID i and j. Use networkx to generate a randome network of size N. Output all possible routes from node i to node j which are less or equal to D.
5.2.3 Material
Python Official Website: https://www.python.org
Python Official Documentation: https://docs.python.org/3/tutorial
Anaconda Python distribution: https://www.anaconda.com/distribution
5.2.4 Literature
[1] J. Bovy. galpy: A python LIBRARY FOR GALACTIC DYNAMICS. The Astrophysical Journal Sup- plement Series, 216(2):29, feb 2015.
[2] G. van Rossum and P. D. Team. Python 3.6 Extending and Embedding Python. Samurai Media Limited, United Kingdom, 2016.
5.3 C03 Machine Learning and Neural Network
5.3.1 Group Exercise
C03-1-1. Collect features: height, weight, play football (0/1), swim (0/1), CET4 (0/1), wash clothes more than once a month (0/1), daily sleeping hours, winning of scholarships (0/1), and gender (F/M). Use SVM to classify genders.
12 Chapter 5. Content
C03-2-1. Use NN to do the gender classfication.
C03-2-2. Prepare some 28X28 figures of handwritings of number ‘0’ and ‘1’. Use NN to classify the two numbers.
5.3.2 Homework
C03-1-1. Implement a SVM to classify wine using the wine dataset available at https://archive.ics.uci.edu/ml/datasets/ Wine .
C03-2-1. Use NN to do the wine classification again.
5.3.3 Material
5.3.4 Literature
[1] J. Chiles, S. M. Buckley, S. W. Nam, R. P. Mirin, and J. M. Shainline. Design, fabrication, and metrology of 10 x 100 multi-planar integrated photonic routing manifolds for neural networks. APL Photonics, 3(10):106101, 2018.
[2] T. Gebru, J. Krause, Y. Wang, D. Chen, J. Deng, E. L. Aiden, and L. Fei-Fei. Using deep learning and google street view to estimate the demographic makeup of neighborhoods across the united states. Proceedings of the National Academy of Sciences, 2017.
[3] N. Jean, M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon. Combining satellite imagery and machine learning to predict poverty. Science, 353(6301):790–794, 2016.
[4] M. I. Jordan and T. M. Mitchell. Machine learning: Trends, perspectives, and prospects. Science, 349(6245):255–260, 2015.
[5] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc., 2012.
[6] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521:436 EP –, 05 2015.
[7] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, Nov 1998.
[8] M. S. Norouzzadeh, A. Nguyen, M. Kosmala, A. Swanson, M. S. Palmer, C. Packer, and J. Clune. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences, 2018.
[9] W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer. Deep learning massively accelerates super- resolution localization microscopy. Nature Biotechnology, 36:460, Apr. 2018.
[10] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis. Mastering the game of go with deep neural networks and tree search. Nature, 529:484, Jan. 2016.
[11] M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In D. Fleet, T. Pa- jdla, B. Schiele, and T. Tuytelaars, editors, Computer Vision – ECCV 2014, pages 818–833, Cham, 2014. Springer Interna- tional Publishing.
5.3. C03 Machine Learning and Neural Network 13
5.4 C04 Deep Learning
5.4.1 Group Exercise
C04-1-1. Collect pictures of your hands. Use neural network to classify left or right.
C04-2-1. Segmentation of book in pictures. Use Unet.
5.4.2 Homework
1.Design a research, write one-page report discussing the data and possible research questions.
2.Study one of the following literatures and write one-page comments.
Choose either 1 or 2 as your homework.
5.4.3 Literature
[1] Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer MS, Packer C and Clune J (2018), “Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning”, Proceedings of the National Academy of Sciences. National Academy of Sciences.
[2] Jean N, Burke M, Xie M, Davis WM, Lobell DB and Ermon S (2016), “Combining satellite imagery and machine learning to predict poverty”, Science. Vol. 353(6301), pp. 790-794. American Association for the Advancement of Science.
[3] Ouyang W, Aristov A, Lelek M, Hao X and Zimmer C (2018), “Deep learning massively accelerates super- resolution localization microscopy”, Nature Biotechnology., April, 2018. Vol. 36, pp. 460. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved..
[4] He K, Zhang X, Ren S and Sun J (2016), “Deep Residual Learning for Image Recognition”, In The IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR)., June, 2016.
[5] Karpathy A and Fei-Fei L (2015), “Deep Visual-Semantic Alignments for Generating Image Descriptions”, In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., June, 2015.
[6] Chiles J, Buckley SM, Nam SW, Mirin RP and Shainline JM (2018), “Design, fabrication, and metrology of 10 × 100 multi-planar integrated photonic routing manifolds for neural networks”, APL Photonics. Vol. 3(10), pp. 106101.
[7] Girshick R (2015), “Fast R-CNN”, In The IEEE International Conference on Computer Vision (ICCV)., December, 2015.
[8] Ren S, He K, Girshick R and Sun J (2015), “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, In Advances in Neural Information Processing Systems 28. , pp. 91-99. Curran Associates, Inc..
[9] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y (2014), “Generative Adversarial Nets”, In Advances in Neural Information Processing Systems 27. , pp. 2672-2680. Curran Associates, Inc..
[10] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A (2015), “Going Deeper With Convolutions”, In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., June, 2015.
[11] Lecun Y, Bottou L, Bengio Y and Haffner P (1998), “Gradient-based learning applied to document recognition”, Proceedings of the IEEE., Nov, 1998. Vol. 86(11), pp. 2278-2324.
[12] Krizhevsky A, Sutskever I and Hinton GE (2012), “ImageNet Classification with Deep Convolutional Neural Networks”, In Advances in Neural Information Processing Systems 25. , pp. 1097-1105. Curran Associates, Inc..
[13] Deng J, Dong W, Socher R, Li L, Kai Li and Li Fei-Fei (2009), “ImageNet: A large-scale hierarchical image database”, In 2009 IEEE Conference on Computer Vision and Pattern Recognition., June, 2009. , pp. 248-255.
[14] Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K and Darrell T (2015), “Long-Term Recurrent Convolutional Networks for Visual Recognition and Description”, In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., June, 2015.
[15] Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Pan- neershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T and Hassabis D (2016), “Mastering the game of Go with deep neural networks and tree search”, Nature., January, 2016. Vol. 529, pp. 484. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved..
[16] Lin T, Maire M, Belongie SJ, Bourdev LD, Girshick RB, Hays J, Perona P, Ramanan D, Dollár P and Zitnick CL (2014), “Microsoft COCO: Common Objects in Context”, CoRR. Vol. abs/1405.0312
[17] Girshick R, Donahue J, Darrell T and Malik J (2014), “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”, In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., June, 2014.
[18] Jaderberg M, Simonyan K, Zisserman A and kavukcuoglu k (2015), “Spatial Transformer Networks”, In Advances in Neural Information Processing Systems 28. , pp. 2017-2025. Curran Associates, Inc..
[19] Ronneberger O, Fischer P and Brox T (2015), “U-Net: Convolutional Networks for Biomedical Image Segmen- tation”, In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham , pp. 234-241. Springer International Publishing.
[20] Gebru T, Krause J, Wang Y, Chen D, Deng J, Aiden EL and Fei-Fei L (2017), “Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States”, Proceedings of the National Academy of Sciences. National Academy of Sciences.
[21] Simonyan K and Zisserman A (2014), “Very Deep Convolutional Networks for Large-Scale Image Recognition”.
[22] Zeiler MD and Fergus R (2014), “Visualizing and Understanding Convolutional Networks”, In Computer Vision – ECCV 2014. Cham , pp. 818-833. Springer International Publishing.
5.5 C05 Deep Learning for Natural Science
5.5.1 Homework
5.5.2 Literature
[1] Tompson J, Schlachter K, Sprechmann P and Perlin K (2017), “Accelerating Eulerian Fluid Simulation with Con- volutional Networks”, In Proceedings of the 34th International Conference on Machine Learning - Volume 70. Sydney, NSW, Australia , pp. 3424-3433. JMLR.org.
[2] Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer MS, Packer C and Clune J (2018), “Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning”, Proceedings of the National Academy of Sciences. Vol. 115(25), pp. E5716-E5725. National Academy of Sciences.
5.5. C05 Deep Learning for Natural Science 15
[3] Mustafa M, Bard D, Bhimji W, Lukic Z, Al-Rfou R and Kratochvil JM (2019), “CosmoGAN: creating high- fidelity weak lensing convergence maps using Generative Adversarial Networks”, Computational Astrophysics and Cosmology. Vol. 6(1), pp. 1.
[4] Severson KA, Attia PM, Jin N, Perkins N, Jiang B, Yang Z, Chen MH, Aykol M, Herring PK, Fraggedakis D, Bazant MZ, Harris SJ, Chueh WC and Braatz RD (2019), “Data-driven prediction of battery cycle life before capacity degradation”, Nature Energy. Vol. 4(5), pp. 383-391.
[5] Guest D, Cranmer K and Whiteson D (2018), “Deep Learning and Its Application to LHC Physics”, Annual Review of Nuclear and Particle Science. Vol. 68(1), pp. 161-181.
[6] Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N and Prabhat (2019), “Deep learning and process understanding for data-driven Earth system science”, Nature. Vol. 566(7743), pp. 195-204.
[7] Goh GB, Hodas NO and Vishnu A (2017), “Deep learning for computational chemistry”, Journal of Computational Chemistry. Vol. 38(16), pp. 1291-1307.
[8] Elton DC, Boukouvalas Z, Fuge MD and Chung PW (2019), “Deep learning for molecular design - a review of the state of the art”, Molecular Systems Design & Engineering., May, 2019. Vol. 4
[9] Lusch B, Kutz JN and Brunton SL (2018), “Deep learning for universal linear embeddings of nonlinear dynamics”, Nature Communications. Vol. 9(1), pp. 4950.
[10] Kutz JN (2017), “Deep learning in fluid dynamics”, Journal of Fluid Mechanics. Vol. 814, pp. 1-4. Cambridge University Press.
[11] Wei O, Aristov A, Lelek M, Xian H and Zimmer C (2018), “Deep learning massively accelerates super-resolution localization microscopy”, Nature Biotechnology. Vol. 36(5)
[12] Rasp S, Pritchard MS and Gentine P (2018), “Deep learning to represent subgrid processes in climate models”, Proceedings of the National Academy of Sciences. Vol. 115(39), pp. 9684-9689. National Academy of Sciences.
[13] Sirignano J and Spiliopoulos K (2018), “DGM: A deep learning algorithm for solving partial differential equations”, Journal of Computational Physics. Vol. 375, pp. 1339 - 1364.
[14] Liu Y-H and van Nieuwenburg EPL (2018), “Discriminative Cooperative Networks for Detecting Phase Transi- tions”, Phys. Rev. Lett.., Apr, 2018. Vol. 120, pp. 176401. American Physical Society.
[15] Ravanbakhsh S, Oliva JB, Fromenteau S, Price L, Ho S, Schneider JG and Póczos B (2016), “Estimating Cosmo- logical Parameters from the Dark Matter Distribution.”, In ICML. , pp. 2407-2416.
[16] Sanchez-Lengeling B and Aspuru-Guzik A (2018), “Inverse molecular design using machine learning: Generative models for matter engineering”, Science. Vol. 361(6400), pp. 360-365. American Association for the Advancement of Science.
[17] Zhou Q, Tang P, Liu S, Pan J, Yan Q and Zhang S-C (2018), “Learning atoms for materials discovery”, Proceed- ings of the National Academy of Sciences. National Academy of Sciences.
[18] He S, Li Y, Feng Y, Ho S, Ravanbakhsh S, Chen W and Póczos B (2019), “Learning to predict the cosmological structure formation”, Proceedings of the National Academy of Sciences. Vol. 116(28), pp. 13825-13832. National Academy of Sciences.
[19] Faber FA, Lindmaa A, von Lilienfeld OA and Armiento R (2016), “Machine Learning Energies of 2 Million Elpasolite (ABC_2D_6) Crystals”, Phys. Rev. Lett.., Sep, 2016. Vol. 117, pp. 135502. American Physical Society.
[20] Bergen KJ, Johnson PA, de Hoop MV and Beroza GC (2019), “Machine learning for data-driven discovery in solid Earth geoscience”, Science. Vol. 363(6433) American Association for the Advancement of Science.
[21] Butler KT, Davies DW, Cartwright H, Isayev O and Walsh A (2018), “Machine learning for molecular and materials science”, Nature. Vol. 559(7715), pp. 547-555.
[22] Zhang Y, Mesaros A, Fujita K, Edkins SD, Hamidian MH, Ch’ng K, Eisaki H, Uchida S, Davis JCS, Khatami E and Kim E-A (2019), “Machine learning in electronic-quantum-matter imaging experiments”, Nature. Vol. 570(7762), pp. 484-490.
[23] Carrasquilla J and Melko RG (2017), “Machine learning phases of matter”, Nature Physics., 02, 2017. Vol. 13, pp. 431 EP -. Nature Publishing Group SN -.
[24] Scandolo S (2019), “Machine learning provides realistic model of complex phase transition”, Proceedings of the National Academy of Sciences. Vol. 116(21), pp. 10204-10205. National Academy of Sciences.
[25] Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G and Ceriotti M (2017), “Machine learning unifies the modeling of materials and molecules”, Science Advances., 12, 2017. Vol. 3(12), pp. e1701816.
[26] Waldmann IP and Griffith CA (2019), “Mapping Saturn using deep learning”, Nature Astronomy. Vol. 3(7), pp. 620-625.
[27] Hartmann MJ and Carleo G (2019), “Neural-Network Approach to Dissipative Quantum Many-Body Dynamics”, Phys. Rev. Lett.., Jun, 2019. Vol. 122, pp. 250502. American Physical Society.
[28] Kates-Harbeck J, Svyatkovskiy A and Tang W (2019), “Predicting disruptive instabilities in controlled fusion plasmas through deep learning”, Nature. Vol. 568(7753), pp. 526-531.
[29] Zahrt AF, Henle JJ, Rose BT, Wang Y, Darrow WT and Denmark SE (2019), “Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning”, Science. Vol. 363(6424) American Association for the Advancement of Science.
[30] Chua AJK, Galley CR and Vallisneri M (2019), “Reduced-Order Modeling with Artificial Neurons for Gravitational-Wave Inference”, Phys. Rev. Lett.., May, 2019. Vol. 122, pp. 211101. American Physical Society.
[31] Baldi P, Sadowski P and Whiteson D (2014), “Searching for exotic particles in high-energy physics with deep learning”, Nature Communications., 07, 2014. Vol. 5, pp. 4308 EP -. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. SN -.
[32] Han J, Jentzen A and Weinan E (2018), “Solving high-dimensional partial differential equations using deep learning”, Proceedings of the National Academy of Sciences. Vol. 115(34), pp. 8505-8510. National Academy of Sciences.
[33] Wu D, Wang L and Zhang P (2019), “Solving Statistical Mechanics Using Variational Autoregressive Networks”, Phys. Rev. Lett.., Feb, 2019. Vol. 122, pp. 080602. American Physical Society.
[34] Agostinelli F, McAleer S, Shmakov A and Baldi P (2019), “Solving the Rubik’s cube with deep reinforcement learning and search”, Nature Machine Intelligence. Vol. 1(8), pp. 356-363.
[35] Fredericksen MA, Zhang Y, Hazen ML, Loreto RG, Mangold CA, Chen DZ and Hughes DP (2017), “Three- dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manip- ulated ants”, Proceedings of the National Academy of Sciences. Vol. 114(47), pp. 12590-12595. National Academy of Sciences.
5.6 C06 Deep Learning for Life Science
5.6.1 Homework
Design an AI application for healthcare, write one page discussing what/why/how/who
5.6.2 Literature
[1] Zou J and Schiebinger L (2018), AI can be sexist and racist—it’s time to make it fair.
5.6. C06 Deep Learning for Life Science 17
[2] Tomav sev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Mottram A, Meyer C, Ravuri S, Protsyuk I, Connell A, Hughes CO, Karthikesalingam A, Cornebise J, Montgomery H, Rees G, Laing C, Baker CR, Peterson K, Reeves R, Hassabis D, King D, Suleyman M, Back T, Nielson C, Ledsam JR and Mohamed S (2019), “A clinically applicable approach to continuous prediction of future acute kidney injury”, Nature. Vol. 572(7767), pp. 116-119.
[3] Zou J, Huss M, Abid A, Mohammadi P, Torkamani A and Telenti A (2019), “A primer on deep learning in genomics”, Nature Genetics. Vol. 51(1), pp. 12-18.
[4] Coley CW, Thomas DA, Lummiss JAM, Jaworski JN, Breen CP, Schultz V, Hart T, Fishman JS, Rogers L, Gao H, Hicklin RW, Plehiers PP, Byington J, Piotti JS, Green WH, Hart AJ, Jamison TF and Jensen KF (2019), “A robotic platform for flow synthesis of organic compounds informed by AI planning”, Science. Vol. 365(6453) American Association for the Advancement of Science.
[5] Mamoshina P, Vieira A, Putin E and Zhavoronkov A (2016), “Applications of Deep Learning in Biomedicine”, Molecular Pharmaceutics. Vol. 13(5), pp. 1445-1454.
[6] Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P and Zhavoronkov A (2016), “Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data”, Molecular Pharmaceutics., In Molecular Pharmaceutics., 07, 2016. Vol. 13(7), pp. 2524-2530. American Chemical Society.
[7] Angermueller C, Pärnamaa T, Parts L and Stegle O (2016), “Deep learning for computational biology”, Molecular Systems Biology. Vol. 12(7) EMBO Press.
[8] Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B and Yang G (2017), “Deep Learning for Health Informatics”, IEEE Journal of Biomedical and Health Informatics., Jan, 2017. Vol. 21(1), pp. 4-21.
[9] Kim HK, Min S, Song M, Jung S, Choi JW, Kim Y, Lee S, Yoon S and Kim H(H (2018), “Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity”, Nature Biotechnology. Vol. 36, pp. 239. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved..
[10] Min S, Lee B and Yoon S (2017), “Deep learning in bioinformatics”, Briefings in Bioinformatics. Vol. 18(5), pp. 851-869.
[11] Gawehn E, Hiss JA and Schneider G (2016), “Deep Learning in Drug Discovery”, Molecular Informatics. Vol. 35(1), pp. 3-14.
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[23] Ren J, Ahlgren NA, Lu YY, Fuhrman JA and Sun F (2017), “VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data”, Microbiome. Vol. 5(1), pp. 69.
5.7 C07 Deep Learning for Social Science
5.7.1 Homework
5.7.2 Literature
[1] Jean N, Burke M, Xie M, Davis WM, Lobell DB and Ermon S (2016), “Combining satellite imagery and machine learning to predict poverty”, Science. Vol. 353(6301), pp. 790-794. American Association for the Advancement of Science.
[2] Lazer D, Pentland A, Adamic L, Aral S, Barabási A-L, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M, Jebara T, King G, Macy M, Roy D and Van Alstyne M (2009), “Computational Social Science”, Science. Vol. 323(5915), pp. 721-723. American Association for the Advancement of Science.
[3] Dong L, Ratti C and Zheng S (2019), “Predicting neighborhoods socioeconomic attributes using restaurant data”, Proceedings of the National Academy of Sciences. Vol. 116(31), pp. 15447-15452. National Academy of Sciences.
[4] Einav L, Finkelstein A, Mullainathan S and Obermeyer Z (2018), “Predictive modeling of U.S. health care spending in late life”, Science. Vol. 360(6396), pp. 1462-1465. American Association for the Advancement of Science.
[5] Heftneal S, Burney J, Bendavid E and Burke M (2018), “Robust relationship between air quality and infant mor- tality in Africa”, Nature.
[6] McFarland DA, Lewis K and Goldberg A (2016), “Sociology in the Era of Big Data: The Ascent of Forensic Social Science”, The American Sociologist., Mar, 2016. Vol. 47(1), pp. 12-35.
[7] Gebru T, Krause J, Wang Y, Chen D, Deng J, Aiden EL and Fei-Fei L (2017), “Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States”, Proceedings of the National Academy of Sciences. Vol. 114(50), pp. 13108-13113. National Academy of Sciences.
5.8 C08 Deep Learning for Management Science
5.8.1 Homework
5.8.2 Literature
[1] Abdollahi M, Khaleghi T and Yang K (2020), “An integrated feature learning approach using deep learning for travel time prediction”, Expert Systems with Applications. Vol. 139, pp. 112864.
[2] Fagnan DE, Fernandez JM, Lo AW and Stein RM (2013), “Can Financial Engineering Cure Cancer?”, The Amer- ican Economic Review. Vol. 103(3), pp. 406-411. American Economic Association.
[4] Hanaki N, Peterhansl A, Dodds PS and Watts DJ (2007), “Cooperation in Evolving Social Networks”, Management Science. Vol. 53(7), pp. 1036-1050.
[5] Kraus M and Feuerriegel S (2017), “Decision support from financial disclosures with deep neural networks and transfer learning”, Decision Support Systems. Vol. 104, pp. 38 - 48.
[6] Guan Y, Wei Q and Chen G (2019), “Deep learning based personalized recommendation with multi-view information integration”, Decision Support Systems. Vol. 118, pp. 58 - 69.
[7] Do HH, Prasad P, Maag A and Alsadoon A (2019), “Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review”, Expert Systems with Applications. Vol. 118, pp. 272 - 299.
[8] Vo NN, He X, Liu S and Xu G (2019), “Deep learning for decision making and the optimization of socially responsible investments and portfolio”, Decision Support Systems. Vol. 124, pp. 113097.
[9] Dabiri S and Heaslip K (2019), “Developing a Twitter-based traffic event detection model using deep learning architectures”, Expert Systems with Applications. Vol. 118, pp. 425 - 439.
[10] Santos FC, Pacheco JM and Lenaerts T (2006), “Evolutionary dynamics of social dilemmas in structured heterogeneous populations”, Proceedings of the National Academy of Sciences. Vol. 103(9), pp. 3490-3494. National Academy of Sciences.
[11] Loureiro A, Miguéis V and da Silva LF (2018), “Exploring the use of deep neural networks for sales forecasting in fashion retail”, Decision Support Systems. Vol. 114, pp. 81 - 93.
[12] Gracia-Lázaro C, Ferrer A, Ruiz G, Tarancón A, Cuesta JA, Sánchez A and Moreno Y (2012), “Heterogeneous networks do not promote cooperation when humans play a Prisoner’s Dilemma”, Proc Natl Acad Sci USA., August, 2012. Vol. 109(32), pp. 12922.
[13] Traulsen A, Semmann D, Sommerfeld RD, Krambeck H-J and Milinski M (2010), “Human strategy updating in evolutionary games”, Proceedings of the National Academy of Sciences. National Academy of Sciences.
[14] Moews B, Herrmann JM and Ibikunle G (2019), “Lagged correlation-based deep learning for directional trend change prediction in financial time series”, Expert Systems with Applications. Vol. 120, pp. 197 - 206.
[15] Wang Y and Xu W (2018), “Leveraging deep learning with LDA-based text analytics to detect automobile insur- ance fraud”, Decision Support Systems. Vol. 105, pp. 87 - 95.
5.8. C08 Deep Learning for Management Science 23
[16] Shirado H and Christakis NA (2017), “Locally noisy autonomous agents improve global human coordination in network experiments”, Nature., May, 2017. Vol. 545, pp. 370. Macmillan Publishers Limited, part of Springer Nature. All rights reserved..
[17] Baum JAC, Cowan R and Jonard N (2010), “Network-Independent Partner Selection and the Evolution of Inno- vation Networks”, Management Science. Vol. 56(11), pp. 2094-2110.
[18] Das S, Rousseau R, Adamson PC and Lo AW (2018), “New business models to accelerate innovation in pediatric oncology therapeutics: A review”, JAMA Oncology.
[19] Liu Y (2019), “Novel volatility forecasting using deep learning–Long Short Term Memory Recurrent Neural Networks”, Expert Systems with Applications. Vol. 132, pp. 99 - 109.
[20] Hauert C and Doebeli M (2004), “Spatial structure often inhibits the evolution of cooperation in the snowdrift game”, Nature., April, 2004. Vol. 428, pp. 643. Macmillan Magazines Ltd..
[21] Farmer JD and Foley D (2009), “The economy needs agent-based modelling”, Nature., 08, 2009. Vol. 460(7256), pp. 685-686. Nature Publishing Group.
[22] Gallo E and Yan C (2015), “The effects of reputational and social knowledge on cooperation”, Proceedings of the National Academy of Sciences. National Academy of Sciences.
[23] Da’u A, Salim N, Rabiu I and Osman A (2019), “Weighted Aspect-Based Opinion Mining Using Deep Learning for Recommender System”, Expert Systems with Applications. , pp. 112871.
5.9 C09 Summary
5.9.1 Homework
5.9.2 Literature
[1] Baum JAC, Cowan R and Jonard N (2010), “Network-Independent Partner Selection and the Evolution of Innova- tion Networks”, Management Science. Vol. 56(11), pp. 2094-2110.
[2] Das S, Rousseau R, Adamson PC and Lo AW (2018), “New business models to accelerate innovation in pediatric oncology therapeutics: A review”, JAMA Oncology.
[3] Fagnan DE, Fernandez JM, Lo AW and Stein RM (2013), “Can Financial Engineering Cure Cancer?”, The Amer- ican Economic Review. Vol. 103(3), pp. 406-411. American Economic Association.
[4] Farmer JD and Foley D (2009), “The economy needs agent-based modelling”, Nature., 08, 2009. Vol. 460(7256), pp. 685-686. Nature Publishing Group.
[5] Gallo E and Yan C (2015), “The effects of reputational and social knowledge on cooperation”, Proceedings of the National Academy of Sciences. National Academy of Sciences.
[6] Gracia-Lázaro C, Ferrer A, Ruiz G, Tarancón A, Cuesta JA, Sánchez A and Moreno Y (2012), “Heterogeneous networks do not promote cooperation when humans play a Prisoner’s Dilemma”, Proc Natl Acad Sci USA., August, 2012. Vol. 109(32), pp. 12922.
[7] Hanaki N, Peterhansl A, Dodds PS and Watts DJ (2007), “Cooperation in Evolving Social Networks”, Management Science. Vol. 53(7), pp. 1036-1050.
[8] Hauert C and Doebeli M (2004), “Spatial structure often inhibits the evolution of cooperation in the snowdrift game”, Nature., April, 2004. Vol. 428, pp. 643. Macmillan Magazines Ltd..
[10] Santos FC, Pacheco JM and Lenaerts T (2006), “Evolutionary dynamics of social dilemmas in structured heterogeneous populations”, Proceedings of the National Academy of Sciences. Vol. 103(9), pp. 3490-3494. National Academy of Sciences.
[11] Shirado H and Christakis NA (2017), “Locally noisy autonomous agents improve global human coordination in network experiments”, Nature., May, 2017. Vol. 545, pp. 370. Macmillan Publishers Limited, part of Springer Nature. All rights reserved..
[12] Traulsen A, Semmann D, Sommerfeld RD, Krambeck H-J and Milinski M (2010), “Human strategy updating in evolutionary games”, Proceedings of the National Academy of Sciences. National Academy of Sciences.
5.9. C09 Summary 25
C04 Deep Learning
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Artificial Intelligence Documentation

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