Courses_in_ML_DL5
https://twitter.com/ujjwalscript/status/1588056009137827840?t=myZbTsa7NymiBcTTEcg0gQ&s=08
dlsyscourse.org Deep Learning Systems course https://www.youtube.com/channel/UC3-KIvmiIaZimgXMNt7F99g/videos
Imperial College London Math 2 Part B Probability and Statistics for Engineers fall 2019, by Bruno Clerckx http://www.ee.ic.ac.uk/bruno.clerckx/EE_fitted.pdf https://www.youtube.com/playlist?list=PL3nE1Yo1b4CpcEOgS3s80YUys7nGRqVPM
UIUC CS / ECE 434 Mobile Computing Algorithms and Applications spring 2020, 2021, by Romit Roy Choudhury https://courses.grainger.illinois.edu/cs434/sp2021/ https://courses.grainger.illinois.edu/cs434/sp2020/
UC Irvine CS 276 Reasoning in Graphical Models spring 2018, by Rina Dechter https://www.ics.uci.edu/~dechter/courses/ics-276/spring-18/
UC Irvine Math 4 Math for Economists Summer 2013, by Jason Kronewetter https://www.youtube.com/playlist?list=PLqOZ6FD_RQ7n8yvjW0DAxRAmou8EOzbpD
UC Irvine Math 176 Math of Finance 2014, by Donald Saari https://www.youtube.com/playlist?list=PLqOZ6FD_RQ7lTg3D3k3TSj8ApXpT-b9kW
UC Irvine Physics 50 Math Methods spring 2014, by Michael Dennin https://www.youtube.com/playlist?list=PLqOZ6FD_RQ7nHzkddoGu8Qov2rLweTPyk
UC Irvine Math 131A Introduction to Probability and Statistics summer 2013, by Michael Cranston https://www.youtube.com/playlist?list=PLqOZ6FD_RQ7n6XnvxxsWfxFtYf0Xj479J
UC Irvine Math 131B Introduction to Probability and Statistics summer 2013, by Michael Cranston https://www.youtube.com/playlist?list=PLqOZ6FD_RQ7kcLcp0qHUDOp6f5JKmA8Aq
UC Irvine Math 113B Mathematical Biology winter 2014, by German A. Enciso https://www.youtube.com/playlist?list=PLqOZ6FD_RQ7lnGZ7fkn503y_7U4rrJ-Se
UW Fish 507 Applied Time Series Analysis spring 2019, 2021, by Mark Scheuerell, Elizabeth (Eli) Holmes, Eric Ward https://atsa-es.github.io/atsa/lectures.html https://www.youtube.com/playlist?list=PLA5yNsxyt7sADlCCh3PZ5S4e1kEOSj9jq
University of Alberta Stat 479 Time Series Analysis winter 2020, by Adam Kashlak https://www.youtube.com/playlist?list=PL5ND7pZq8F20u5aoYyI24dNHXfOrM80yA https://sites.ualberta.ca/~kashlak/data/stat479.pdf
University of Tübingen S411 Advanced Time Series Analysis in Economics and Finance spring 2020, by Joachim Grammig https://timms.uni-tuebingen.de/tp/UT_20201102_001_ws2021atsa_0001
Financial Time Series Modeling and Forecasting 2013, by Myung-Jig Kim https://www.youtube.com/playlist?list=PLSN_PltQeOyiG_wobD2ZzqX5lEFF1oJQk
Davidson CSC 381: Deep Learning, Fall 2022 https://www.youtube.com/playlist?list=PLgPbN3w-ia_PeT1_c5jiLW3RJdR7853b9
Deep Foundations https://www.youtube.com/playlist?list=PLVYTJAasVdWwqIOgf-Lz7GPAgoKBADNyi
Parallel Computer Architecture and Programming http://15418.courses.cs.cmu.edu/spring2016/lectures
CMU 15 816 Linear Logic spring 2012, by Frank Pfenning https://www.cs.cmu.edu/~fp/courses/15816-s12/schedule.html
The Turing Data Science Classes 2018 https://www.youtube.com/playlist?list=PLuD_SqLtxSdWcl2vx4K-0mSflRRLyfwlJ
UvA Introduction to Information Theory fall 2019, by Michael Walter https://qi.ruhr-uni-bochum.de/iit19/ https://biaslab.github.io/teaching/archive/bmlip-2021/
TUe Bayesian Machine Learning and Information Processing 2021, 2022, by Bert de Vries https://www.youtube.com/playlist?list=PLkDDgjnfkmCH4zq9_ul-KaTsk-1j1EVEo
https://k-means-explorable.vercel.app/
CMU 16 715 Advanced Robot Dynamics and Simulation fall 2022, by Zac Manchester https://github.com/dynamics-simulation-16-715 https://www.youtube.com/playlist?list=PLZnJoM76RM6ItAfZIxJYNKdaR_BobleLY
CMU 16 745 Optimal Control and Reinforcement Learning spring 2022, by Zac Manchester https://www.youtube.com/playlist?list=PLZnJoM76RM6Iaf59ICcU9-DzztGZvK_52
CMU 16 899 Adaptive Control and Reinforcement Learning fall 2020, by Changliu Liu http://www.cs.cmu.edu/~cliu6/acrl-fall20.html https://www.youtube.com/playlist?list=PLZL5VXraKdz-0zByoPNzNTqSirR4veU8z
MIT 2.160 Identification, Estimation, and Learning fall 2020, by Harry Asada https://www.youtube.com/playlist?list=PLDHxS-d2mJqup7SwhgQjqCkXiDKo4rcL6 https://mit.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx#folderID=%22e673193a-a503-4485-819f-ac32016cbc1b%22
University of Freiburg Numerical Optimization ws 2015, 2020, by Moritz Diehl https://www.syscop.de/teaching/ws2020/numerical-optimization https://www.syscop.de/teaching/ws2015/numopt
University of Freiburg Modeling and System Identification ws 2017, 2018, 2019, 2020, 2021, by Moritz Diehl https://www.syscop.de/teaching/ws2017/msi https://www.syscop.de/teaching/ws2018/modeling-and-system-identification https://www.syscop.de/teaching/ws2019/msi https://www.syscop.de/teaching/ws2020/msi https://www.syscop.de/teaching/ws2021/modeling-and-system-identification
University of Freiburg Numerical Optimal Control ss 2017, 2021, by Moritz Diehl https://www.syscop.de/teaching/ss2017/numerical-optimal-control https://www.syscop.de/teaching/ss2021/numerical-optimal-control
University of Freiburg Model Predictive Control and Reinforcement Learning ss 2021, by Moritz Diehl, Joschka Boedecker, Sebastien Gros, Sergey Levine https://www.syscop.de/teaching/ss2021/model-predictive-control-and-reinforcement-learning
University of Freiburg State Space Control Systems ss 2019, by Moritz Diehl, Dang Doan https://www.syscop.de/teaching/ss2019/state-space-control-systems
University of Freiburg Wind Energy Systems ss 2018, 2020, 2022, by Moritz Diehl https://www.syscop.de/teaching/ss2020/wind-energy-systems https://www.syscop.de/teaching/ss2022/wind-energy-systems https://www.syscop.de/teaching/ss2018/wind-energy-systems
University of Cambridge Mobile Robot Systems, by Amanda Prorok https://www.youtube.com/playlist?list=PLaTKfS3-bDpDyOwrxLcQRGxY9XJw33ANo
University of Freiburg Introduction to Mobile Robotics SS 2014,2015, 2016, 2017, by Wolfram Burgard, Maren Bennewitz, Gian Diego Tipaldi, Michael Ruhnke, Daniel Büscher http://ais.informatik.uni-freiburg.de/teaching/ss14/robotics/ http://ais.informatik.uni-freiburg.de/teaching/ss17/robotics/ http://ais.informatik.uni-freiburg.de/teaching/ss16/robotics/ http://ais.informatik.uni-freiburg.de/teaching/ss15/robotics/
University of Freiburg Robot Mapping WS 2014, 2015, 2016, 2017, by Gian Diego Tipaldi, Luciano Spinello, Wolfram Burgard, Daniel Büscher http://ais.informatik.uni-freiburg.de/teaching/ws14/mapping/ http://ais.informatik.uni-freiburg.de/teaching/ws15/mapping/ http://ais.informatik.uni-freiburg.de/teaching/ws16/mapping/ http://ais.informatik.uni-freiburg.de/teaching/ws17/mapping/
CS324 - Large Language Models https://stanford-cs324.github.io/winter2022/
CS25: Transformers United https://web.stanford.edu/class/cs25/
Advanced Natural Language Processing https://people.cs.umass.edu/~miyyer/cs685/
COS 597G (Fall 2022): Understanding Large Language Models https://www.cs.princeton.edu/courses/archive/fall22/cos597G/
COMP790: (Deep) Learning with Limited Labeled Data (DL3D) https://github.com/craffel/dl3d-seminar
CSCI 601.771: Self-supervised Statistical Models https://self-supervised.cs.jhu.edu/
COMP790-101: Large Language Models https://github.com/craffel/llm-seminar/
Course Title: CS 6604: Data Challenges in Machine Learning (Spring 2021) https://isminoula.github.io/cs6604SP21/
NOC:Applied Accelerated Artificial Intelligence, IIT Madras https://nptel.ac.in/courses/106106238
CMU 10 708 Probabilistic Graphical Models spring 2018, by Kayhan Batmanghelich https://www.youtube.com/channel/UCBzyxSQ13mdS4vNCuYPuRWg/videos
https://inst.eecs.berkeley.edu/~eecs151/fa22/
https://inst.eecs.berkeley.edu/~eecs251b/sp22/
https://inst.eecs.berkeley.edu/~eecs151/fa22/
CMU 10 708 Probabilistic Graphical Models spring 2017, 2019, 2020 by Eric Xing https://www.cs.cmu.edu/~epxing/Class/10708-20/lectures.html https://www.cs.cmu.edu/~epxing/Class/10708-19/lectures/ https://www.cs.cmu.edu/~epxing/Class/10708-17/lecture.html
University of Oxford Scientific Computing for DPhil Students 2019, by Nick Trefethen https://podcasts.ox.ac.uk/series/scientific-computing-dphil-students
UIUC CS 554 / CSE 512 Parallel Numerical Algorithms Fall 2017, by Edgar Solomonik https://solomonik.cs.illinois.edu/teaching/cs554/index.html
UC Berkeley CS 267 Applications of Parallel Computers spring 2021, by Kathy Yelick, Aydin Buluc, Jim Demmel https://sites.google.com/lbl.gov/cs267-spr2021
UC Berkeley EE 290-2 Hardware for Machine Learning spring 2021, by Sophia Shao https://inst.eecs.berkeley.edu/~ee290-2/sp21/
UC Berkeley EE 290-005 Integrated Perception, Learning, and Control spring 2021, by Yi Ma, Jitendra Malik, Shankar Sastry, Claire Tomlin https://pages.github.berkeley.edu/ee290-005/sp21-site/calendar/
UC Berkeley EE 106a / 206a Introduction to Robotics fall 2019, by Shankar Sastry https://ucb-ee106.github.io/ee106a-fa19/calendar/
ETH Zurich Mathematics of Data Science spring 2021, by Afonso S. Bandeira ETH Zurich Mathematics of Machine Learning (first half) spring 2021, by Afonso S. Bandeira https://www.youtube.com/playlist?list=PLiud-28tsatL0MbfJFQQS7MYkrFrujCYp https://www.youtube.com/playlist?list=PLiud-28tsatIKUitdoH3EEUZL-9i516IL
Machine Learning in Production / AI Engineering (17-445/17-645/17-745/11-695) https://ckaestne.github.io/seai/
ETH Zurich Information Systems for Engineers - Fall 2021, by Ghislain Fourny https://www.youtube.com/playlist?list=PLs5KPrcFtb0VaveUT-vsBswwl7RWvFeNf
ETH Zurich Information Retrieval Spring 2022, by Ghislain Fourny https://www.youtube.com/playlist?list=PLs5KPrcFtb0UtG4ILOX27gDbUivxW5QUT
ETH Zurich Big Data for Engineers Spring 2022, by Ghislain Fourny https://www.youtube.com/playlist?list=PLs5KPrcFtb0XU-muVd8nkokMXNJ1GFx7e
ETH Zurich Big Data Fall 2021, by Ghislain Fourny https://www.youtube.com/playlist?list=PLs5KPrcFtb0VFulL1SbviJBIMi9pDKYZ2
ETH Zurich Statistical Learning Theory spring 2019, by Joachim M. Buhmann https://video.ethz.ch/lectures/d-infk/2019/spring/252-0526-00L.html
https://d2cml-ai.github.io/14.388_r/intro.html
https://www.ics.uci.edu/~dechter/courses/ics-295cr/spring-2021/ https://www.youtube.com/channel/UCqza2pfAex9J480BFDMKLsg/playlists
ETH Zurich Fundamentals of Mathematical Statistics fall 2021, by Sara van de Geer https://video.ethz.ch/lectures/d-math/2021/autumn/401-3621-00L.html
ETH Zurich Numerical Methods for CSE fall 2015, by Ralf Hiptmair https://video.ethz.ch/lectures/d-math/2015/autumn/401-0663-00L.html
ETH Zurich Numerical Methods for Partial Differential Equations spring 2016, by Ralf Hiptmair https://video.ethz.ch/lectures/d-math/2016/spring/401-0674-00L.html
ETH Zurich Data Modelling and Databases spring 2022, by Ce Zhang https://video.ethz.ch/lectures/d-infk/2022/spring/252-0063-00L.html
ETH Zurich Computational Semantics for Natural Language Processing spring 2022, by Mrinmaya Sachan https://video.ethz.ch/lectures/d-infk/2022/spring/263-5000-00L.html
ETH Zurich Deep Learning fall 2019, by Thomas Hofmann https://video.ethz.ch/lectures/d-infk/2019/autumn/263-3210-00L.html
ETH Zurich Advanced Machine Learning fall 2019, by Joachim M. Buhmann https://video.ethz.ch/lectures/d-infk/2019/autumn/252-0535-00L.html
Harvard University CS 229br Advanced Topics in the theory of machine learning spring 2021, by Boaz Barak https://boazbk.github.io/mltheoryseminar/cs229br.html
Lesa Hoffman https://www.youtube.com/channel/UCHi-jk4KXUJN7WXeABtL4Cg/playlists
Approximation Algorithms, Copenhagen University, Spring 2021 https://rasmuspagh.net/courses/APX21/
https://www.cs.utexas.edu/~gdurrett/courses/online-course/materials.html https://www.youtube.com/playlist?list=PLofp2YXfp7Tbk88uH4jejfXPd2OpWuSLq https://www.cs.utexas.edu/~gdurrett/teaching.shtml
Video lectures, UC Irvine CS 276 Reasoning in Graphical Models fall 2021, by Rina Dechter https://www.ics.uci.edu/~dechter/courses/ics-276/fall-2021/
Video lectures, UC Irvine CS 275 Constraint Networks fall 2020, by Rina Dechter https://www.ics.uci.edu/~dechter/courses/ics-275/fall-2020/
Video lectures, Cornell ORIE 4580 / 5580 Simulation Modeling and Analysis, ORIE 5581 Monter Carlo Simulation fall 2020, by Sid Banarjee https://vod.video.cornell.edu/channel/Monte%2BCarlo%2BSimulation%2B%2528ORIE%2B4580%2B-%2BFall%2B2020%2529/251110893 https://sidbanerjee.orie.cornell.edu/courses/orie4580f20/
https://sidbanerjee.orie.cornell.edu/courses/orie4742s21/
https://people.orie.cornell.edu/mru8/orie4741/lectures.html
Video lectures, CMU 17-445 / 10-645 Software Engineering for AI-Enabled Systems summer 2020, by Christian Kästner
https://ckaestne.github.io/seai/S2020/#course-content
http://www.cs.cmu.edu/~mgormley/courses/10418/schedule.html
https://www.math.ucdavis.edu/~deloera/TEACHING/VIDEOS/
https://video.ucdavis.edu/channel/MATH168-+Mathematical+Optimization/193424153
https://github.com/ivan-bilan/The-NLP-Pandect
http://www.ricardocalix.com/MLfoundations/MLfoundations.htm
http://www.ricardocalix.com/pytorchML/course1.htm
https://laurentlessard.com/teaching/524-intro-to-optimization/
"Understanding Deep Learning" and will be published by MIT press. A partial draft is now available at:
https://udlbook.github.io/udlbook/
https://www.youtube.com/playlist?list=PL3xCBlatwrsU_HKkwf6RlxvErUHu6FPVV https://www.cse.iitm.ac.in/~rupesh/events/gpu2022/
https://fpcv.cs.columbia.edu/ https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw/playlists
https://www.cs.ubc.ca/~schmidtm/Courses/LecturesOnML/
All What you Need to Know about Vision Transformers. Check out my Advance Computer Vision Course: https://crcv.ucf.edu/courses/cap6412-spring-2022/schedule/ We discussed all relevant papers. Youtube video presentations: https://www.crcv.ucf.edu/courses/cap6412-spring-2022/schedule/
https://fleuret.org/dlc/#lecture-13
https://ds100.org/su19/syllabus https://olebo.github.io/textbook/ https://www.textbook.ds100.org/intro.html
https://mltechniques.com/2022/06/13/math-for-machine-learning-12-must-read-books/
https://bipinkrishnan.github.io/ml-recipe-book/about.html
https://drive.google.com/drive/folders/1iW7IPrVUqsHumgXUMH_rgeBLpJjRDCmJ
https://www.youtube.com/c/GhislainFournysLectures/playlists
https://interpretable-ml-class.github.io/
https://www.coursicle.com/harvard/courses/STAT/195/
https://github.com/dalmia/Deep-Learning-Book-Chapter-Summaries
https://dlsyscourse.org/lectures/
https://andrejristeski.github.io/10707-S21/syllabus.html https://andrejristeski.github.io/10707-S21/
https://medium.com/mlearning-ai/learn-computer-vision-from-top-universities-bb6019be74d2
https://xcelab.net/rm/statistical-rethinking/ https://shmuma.github.io/rethinking-2ed-julia/
https://www.tml.cs.uni-tuebingen.de/teaching/2020_statistical_learning/ https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC
https://fa.bianp.net/teaching/2018/eecs227at/ http://people.csail.mit.edu/dsontag/courses/ml12/
https://skim.math.msstate.edu/LectureNotes/Machine_Learning_Lecture.pdf https://www.youtube.com/channel/UCmRbK4vlGDht-joOQ5g0J2Q/playlists
https://people.ischool.berkeley.edu/~dbamman/nlpap.html
http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/ http://www.cs.cornell.edu/courses/cs4780/2018fa/ https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
https://www.youtube.com/c/StatisticsofDOOM/playlists https://statisticsofdoom.com/page/human-language-modeling/
https://www.kaggle.com/code/sudalairajkumar/winning-solutions-of-kaggle-competitions/notebook
Probability: https://web.stanford.edu/class/cs109/ AI: https://stanford-cs221.github.io/spring2022/ Machine Learning: https://cs229.stanford.edu Deep Learning: https://cs230.stanford.edu NLP: https://web.stanford.edu/class/cs224n/ Reinforcement Learning: https://web.stanford.edu/class/cs234/
https://mlu-explain.github.io/
https://sites.google.com/view/nsysu-dvlab/courses/computer-vision-2022
https://robsalomone.com/course-deep-probabilistic-models/
https://nlp-css-201-tutorials.github.io/nlp-css-201-tutorials/
https://emtiyaz.github.io/teaching/oist_B39_2022/main.html
https://www.youtube.com/c/K%C3%BCnstlicheIntelligenz/playlists
https://projects.iq.harvard.edu/stat110/home https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo
An ancient Indian memorization secret! https://www.youtube.com/watch?v=3sxhlHrUMog
https://www.youtube.com/watch?v=pH4Pkmey2_E&list=PLdMorpQLjeXmbFaVku4JdjmQByHHqTd1F https://github.com/UMich-CURLY-teaching/UMich-ROB-530-public
http://nsm.iitm.ac.in/cse/#all-services
http://people.eecs.berkeley.edu/~yima/courses/CS294-Spring2022/
https://www.youtube.com/channel/UCQGShzqifIlBMWtH5PPtRXA/playlists
https://www.youtube.com/channel/UCcWc5_rFLDa67oxsFA3YHmg/playlists
http://web.mit.edu/dimitrib/www/RLbook.html
https://www.stat.cmu.edu/~larry/=sml/
https://www.eecs.yorku.ca/~kosta/Courses/EECS4422/ https://www.eecs.yorku.ca/~kosta/Courses/EECS6322/
https://www.youtube.com/channel/UC5gM-GHriu3qw7kKK1wl8zA/playlists
https://www.youtube.com/c/YunWilliamYu/playlists
https://lakens.github.io/statistical_inferences/
https://www.youtube.com/playlist?list=PLmJwSK7qduwWyqcSEB45HOyxq--z8njix
https://www.youtube.com/channel/UCsJsaq9aZfzW_OcYx45LSVQ/playlists
https://ai.science/ https://www.youtube.com/c/AISocraticCirclesAISC/videos
https://mlu-explain.github.io/precision-recall/
https://web.stanford.edu/class/linguist130a/2022/
https://stanford-cs329s.github.io/syllabus.html
https://optimization.cbe.cornell.edu/index.php?title=Main_Page
https://uvaml1.github.io/ https://www.youtube.com/channel/UCsJsaq9aZfzW_OcYx45LSVQ/playlists
https://www.youtube.com/channel/UCezT5wE7O4v4SPWgGeZneJg/playlists https://www.youtube.com/c/CFIIITMadras/playlists
https://www.youtube.com/channel/UC38_LaNUDzCW0XjIEJBS2Lg/videos
https://github.com/dipanjanS/tensorflow2-crash-course
https://www.cs.ryerson.ca/~kosta/CPS843-W2020/
https://www.eecs.yorku.ca/~kosta/Courses/EECS6322/
https://cs.uwaterloo.ca/~lapchi/cs860/
https://ucbrise.github.io/cs294-ai-sys-sp22/
https://www.youtube.com/playlist?list=PL18_rB75vx1PkjXnkX1jiqNeNnVCbNGIh
https://www.cse.iitd.ac.in/~srsarangi/courses/2022/col_720_2022/index.html
https://eligijus112.github.io/api-book/intro.html
https://github.com/UMich-CURLY-teaching/UMich-ROB-530-public https://www.youtube.com/watch?v=pH4Pkmey2_E&list=PLdMorpQLjeXmbFaVku4JdjmQByHHqTd1F&index=1&t=0s
https://drive.google.com/drive/folders/1CgN7DE3pNRNh_4BA_zrrMLqWz6KquwuD
https://github.com/AIPI540/AIPI540-Deep-Learning-Applications
https://www.youtube.com/channel/UCKt-z-AVy7duq6-_N-michg/videos
https://ai-4-all.org/open-learning/ https://www.youtube.com/channel/UCvadXthM88NiUdDUp10e2IQ/videos
https://www.youtube.com/channel/UCX6GGajRdBKTEGhPEF9XptA/playlists
https://mattmasten.github.io/bootcamp/
https://github.com/MacroAnalyst/Linear_Algebra_With_Python
https://github.com/ThinamXx/MachineLearning_DeepLearning https://medium.com/@neuralthreads
https://github.com/craffel/comp790-deep-learning-spring-2022
https://www.youtube.com/watch?v=KZsk8B_z8pI&list=PL5EH0ZJ7V0jV7kMYvPcZ7F9oaf_YAlfbI
https://www.youtube.com/playlist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf
https://mycourses.aalto.fi/course/view.php?id=29632 https://www.youtube.com/playlist?list=PLxSrejNn6xBDgW5fFrhig8n09veeuUBZr
https://github.com/AyswaryaS95/AppliedAI/tree/main/applied_ai_all_files
http://optimizationprinciplesalgorithms.com/
https://github.com/gto76/python-cheatsheet
https://lena-voita.github.io/nlp_course.html
https://linear.axler.net/LADRvideos.html
https://www.youtube.com/channel/UCT0FMBpBkaq3ZP5pnVzSdiA/playlists
https://arthurdouillard.com/deepcourse/
https://www.gatsby.ucl.ac.uk/~gretton/coursefiles/mlss21_taipei.pdf https://www.youtube.com/playlist?list=PLTHUos8oPSUtq6u_SDc5_BaYiJtiVJA-1
https://matlabacademy.mathworks.com/#getting-started
https://farid.berkeley.edu/downloads/tutorials/learnComputerVision/ https://farid.berkeley.edu/downloads/tutorials/learnPython/
https://github.com/edyoda/data-science-complete-tutorial
https://oliviergimenez.github.io/bayesian-stats-with-R/
https://github.com/xbresson/CS5242_2021
https://github.com/khangich/machine-learning-interview
https://www.cs.cmu.edu/~zkolter/course/linalg/
https://github.com/FredAlcantara/AppliedAI
https://course.continualai.org/
https://www.youtube.com/user/minireference/videos
https://github.com/ml874/Cracking-the-Data-Science-Interview
https://www.youtube.com/playlist?list=PLOk2cpmAEiU3YgtHRUm58zGkw66nF2NLZ
https://github.com/rasgointelligence/feature-engineering-tutorials
https://www.youtube.com/channel/UCro89CYRFPJaNppYFuBqoIA/playlists
https://www.youtube.com/playlist?list=PLYFTuCvd-szRNIMZ69lOfm-rt-TKRZ17K
https://www.youtube.com/playlist?list=PLzrCXlf6ypbxS5OYOY3EN_0u2fDuIT6Gt
https://www.aiml.informatik.tu-darmstadt.de/lectures/sml2020sose/index.html
https://canvas.cmu.edu/courses/603/assignments/syllabus https://www.youtube.com/user/professorgeoff/videos
https://github.com/oxford-cs-deepnlp-2017
https://ml-course.github.io/master/
https://www.youtube.com/watch?v=cOHrT9drFFI&list=PLsugXK9b1w1lRzFHm8Z1XXIE8TZBLbjdp
https://www.youtube.com/channel/UCx8BSplARVY8pHKz0SjkMUQ
https://www.youtube.com/channel/UComYuVoXhsH7vcpvZKlXiqw
https://www.youtube.com/c/Insidecode/videos
https://www.youtube.com/channel/UC6U6Iqh6qrPhzAlAWUtrizw/playlists
https://www.youtube.com/channel/UCKRgi-HJDEq0a3nhlG2nQvg/videos
https://www.youtube.com/c/DorienHerremans/playlists
https://www.youtube.com/channel/UC1LUlTdRDXV5u3JxZrHtIsQ/videos
https://www.youtube.com/user/wilvdaalst/playlists
https://www.youtube.com/channel/UCTp91O_mEZCZB6sTR5GSAVw/playlists
https://www.youtube.com/channel/UCH58k2mWfP18JB4tL7rZCXQ/playlists
https://www.youtube.com/channel/UCjHebnevF2qBc_npduwqxBQ/playlists
https://www.youtube.com/c/MilesChen123/playlists
https://www.youtube.com/watch?v=-Fg_P5ANb0s&t=1s https://github.com/statisticalbiotechnology/cb2030
https://www.youtube.com/c/AMGaweda/playlists
https://www.youtube.com/channel/UCu343cR_UtJsKELIBluUXWg/playlists
https://www.youtube.com/c/BachirElKhadir/videos
https://www.youtube.com/c/MutualInformation0/videos
https://www.youtube.com/channel/UC_u4NYtqTOBMxMJRQjaIjUA/videos
https://www.youtube.com/channel/UCRR8OEJ0rbN8BvXQFSD7y6w/videos
https://www.youtube.com/channel/UCXP3hNE5VrquhnptOJOPNag/playlists
https://www.youtube.com/c/Centrederecherchesmath%C3%A9matiquesCRM/playlists
https://www.youtube.com/user/StevenSkiena/playlists
https://www.youtube.com/channel/UCi6dA5kJKCqufp7Kfyuf4Kw/playlists
https://docs.google.com/spreadsheets/d/1AzCODSUL8Os0AR3USO_t0lxRWIgqbQLX4Zg6-u4Fx90/edit#gid=0 https://www.youtube.com/user/RJMarksIII/videos
https://www.youtube.com/channel/UCLGwlAK4v2j35Ie8dbSDo4Q/videos
https://www.youtube.com/c/subbarao2z2/playlists
https://www.youtube.com/channel/UCotztBOmGVl9pPGIN4YqcRw/videos
http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/rkhscourse.html