100 Best Machine Learning Lecture Videos


References:

See also:

100 Best GitHub: Machine Learning | 100 Best Machine Learning Videos | Best Azure Machine Learning Videos | Machine Learning & Chatbots | Machine Learning & Dialog Systems | Machine Learning as a Service (MLaaS) | Machine Learning Meta Guide


allintitle: machine-learning lecture [100x Apr 2015]

  • 10-601 Machine Learning Spring 2015 – Lecture 20

    10-601 Machine Learning Spring 2015 – Lecture 20 Topics: wrap-up of semi-supervised learning, active learning Lecturer: Maria-Florina Balcan. From: Abulhair Saparov Views: 52 2 ratings Time: 01:18:37 More in People & Blogs

  • GA Intro to Data Science – Lecture 6: Intro to Machine Learning and Linear Regression

    GA Intro to Data Science – Lecture 6: Intro to Machine Learning and Linear Regression From: Aerlinger Views: 0 0 ratings Time: 03:12:57 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 19

    10-601 Machine Learning Spring 2015 – Lecture 19 Topics: semi-supervised learning, transductive SVM, co-training Lecturer: Maria-Florina Balcan. From: Abulhair Saparov Views: 74 1 ratings Time: 01:16:25 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 21

    Machine Learning course- Shai Ben-David: Lecture 21 CS 485/685, University of Waterloo. Mar 25, 2015 convex optimization problems, learning and convex loss functions. From: Understanding Machine Learning – Shai Ben-David Views: 24 0 ratings Time: 01:18:58 More in People & Blogs…

  • 10-601 Machine Learning Spring 2015 – Lecture 18

    10-601 Machine Learning Spring 2015 – Lecture 18 Topics: support vector machines (SVM), semi-supervised learning, other learning paradigms Lecturer: Maria-Florina Balcan. From: Abulhair Saparov Views: 194 3 ratings Time: 01:13:18 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 17

    10-601 Machine Learning Spring 2015 – Lecture 17 Topics: kernel methods, margin, kernelizing a learning algorithm Lecturer: Maria-Florina Balcan. From: Abulhair Saparov Views: 244 4 ratings Time: 01:18:10 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 20

    Machine Learning course- Shai Ben-David: Lecture 20 CS 485/685, University of Waterloo. Mar 20, 2015 Convexity of sets and functions. From: Understanding Machine Learning – Shai Ben-David Views: 22 0 ratings Time: 01:15:06 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 19

    Machine Learning course- Shai Ben-David: Lecture 19 CS 485/685, University of Waterloo. Mar 18, 2015 Analyzing and applying AdaBoost. From: Understanding Machine Learning – Shai Ben-David Views: 35 0 ratings Time: 01:17:15 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 15

    10-601 Machine Learning Spring 2015 – Lecture 15 Topics: boosting, weak vs strong PAC learning, analysis of Adaboost Lecturer: Maria-Florina Balcan. From: Abulhair Saparov Views: 245 4 ratings Time: 01:14:07 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 16

    Machine Learning course- Shai Ben-David: Lecture 16 CS 485/685, University of Waterloo. Mar 6, 2015 Computational complexity: Examples of (provably) computationally efficient learning. From: Understanding Machine Learning – Shai Ben-David Views: 35 0 ratings Time: 01:16:21 More in People…

  • Machine Learning course- Shai Ben-David: Lecture 15

    Machine Learning course- Shai Ben-David: Lecture 15 CS 485/685, University of Waterloo. Mar 4, 2015 The Consistency version of learning, and defining the computational complexity of learning. From: Understanding Machine Learning – Shai Ben-David Views: 32 0 ratings Time: 01:18:59 More in …

  • 10-601 Machine Learning Spring 2015 – Lecture 14

    10-601 Machine Learning Spring 2015 – Lecture 14 Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 89 0 ratings Time: 01:21:46 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 14

    Machine Learning course- Shai Ben-David: Lecture 14 CS 485/685, University of Waterloo. Feb 27, 2015 VCdim-based characterization of non-uniform learnability. From: Understanding Machine Learning – Shai Ben-David Views: 48 0 ratings Time: 01:18:31 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 13

    Machine Learning course- Shai Ben-David: Lecture 13 CS 485/685, University of Waterloo. Feb 25, 2015 VCdim-based characterization of non-uniform learnability. From: Understanding Machine Learning – Shai Ben-David Views: 46 0 ratings Time: 01:20:10 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 13

    10-601 Machine Learning Spring 2015 – Lecture 13 Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 292 1 ratings Time: 01:19:47 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 12

    10-601 Machine Learning Spring 2015 – Lecture 12 Topics: inference in graphical models, d-separation, conditional independence Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 182 1 ratings Time: 01:14:20 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 11

    10-601 Machine Learning Spring 2015 – Lecture 11 Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 332 0 ratings Time: 01:15:15 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 12

    Machine Learning course- Shai Ben-David: Lecture 12 CS 485/685, University of Waterloo. Feb13, 2015 A more realistic notion – Non-uniform learnability. From: Understanding Machine Learning – Shai Ben-David Views: 76 0 ratings Time: 01:16:29 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 10

    10-601 Machine Learning Spring 2015 – Lecture 10 Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell. From: Abulhair Saparov Views: 199 3 ratings Time: 01:18:10 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 11

    Machine Learning course- Shai Ben-David: Lecture 11 CS 485/685, University of Waterloo. Feb11, 2015 The Sauer Lemma: Proof and its relevance to sample complexity. From: Understanding Machine Learning – Shai Ben-David Views: 84 1 ratings Time: 01:19:40 More in People & Blogs…

  • 10-601 Machine Learning Spring 2015 – Lecture 9

    10-601 Machine Learning Spring 2015 – Lecture 9 Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension Lecturer: Maria-Florina Balcan. From: Abulhair Saparov Views: 291 2 ratings Time: 01:17:16 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 10

    Machine Learning course- Shai Ben-David: Lecture 10 CS 485/685, University of Waterloo. Feb 6, 2015. Lower bounding the sample complexity of learning in terms of the VC-dimension and the desired accuracy. From: Understanding Machine Learning – Shai Ben-David Views: 71 1 ratings Time: …

  • 10-601 Machine Learning Spring 2015 – Lecture 8

    10-601 Machine Learning Spring 2015 – Lecture 8 Topics: introduction to computational learning theory, statistical learning theory, probably approximately correct (PAC) framework Lecturer: Maria-Florina Balcan. From: Abulhair Saparov Views: 264 5 ratings Time: 01:18:15 More in People &…

  • Machine Learning course- Shai Ben-David: Lecture 9

    Machine Learning course- Shai Ben-David: Lecture 9 CS 485/685, University of Waterloo. Feb 4, 2015. The VC dimension of Linear predictors and the quantitative version of the fundamental theorem (how the VC dimension determines the sample … From: Understanding Machine Learning – Shai Ben-David…

  • 10-601 Machine Learning Spring 2015 – Lecture 7

    10-601 Machine Learning Spring 2015 – Lecture 7 Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 447 2 ratings Time: 01:16:36 More in People & Blogs…

  • Machine Learning course- Shai Ben-David: Lecture 8

    Machine Learning course- Shai Ben-David: Lecture 8 CS 485/685, University of Waterloo. Jan 30, 2015. The relationship of VC dimension and learning: Statement of the “fundamental theorem of statistical machine learning” and a proof of the non-learna… From: Understanding Machine …

  • 10-601 Machine Learning Spring 2015 – Lecture 6

    10-601 Machine Learning Spring 2015 – Lecture 6 Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 509 3 ratings Time: 01:22:40 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 7

    Machine Learning course- Shai Ben-David: Lecture 7 CS 485/685, University of Waterloo. Jan 28, 2015. Introducing the VC-dimension. From: Understanding Machine Learning – Shai Ben-David Views: 101 0 ratings Time: 01:18:33 More in People & Blogs

  • 10-601 Machine Learning Spring 2015 – Lecture 5

    10-601 Machine Learning Spring 2015 – Lecture 5 Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 330 1 ratings Time: 01:20:02 More in People & Blogs

  • Machine Learning course – Shai Ben-David : Lecture 6 by Mohammad-Hassan Zokaei Ashtiani

    Machine Learning course – Shai Ben-David : Lecture 6 by Mohammad-Hassan Zokaei Ashtiani CS 485/685, University of Waterloo. Jan 23, 2015. Learnability of the class of threshold functions and the No-Free-Lunch theorem. From: Understanding Machine Learning – Shai Ben-David Views: 132 0 ratings…

  • 10-601 Machine Learning Spring 2015 – Lecture 4

    10-601 Machine Learning Spring 2015 – Lecture 4 Topics: conditional independence and naive Bayes Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 419 4 ratings Time: 01:20:32 More in People & Blogs

  • Machine Learning course – Shai Ben-David : Lecture 5 by Mohammad-Hassan Zokaei Ashtiani

    Machine Learning course – Shai Ben-David : Lecture 5 by Mohammad-Hassan Zokaei Ashtiani CS 485/685, University of Waterloo. Jan 21, 2015. Proving that every finite class is Agnostically PAC learnable. From: Understanding Machine Learning – Shai Ben-David Views: 225 5 ratings Time: 01:16:36 …

  • 10-601 Machine Learning Spring 2015 – Lecture 3

    10-601 Machine Learning Spring 2015 – Lecture 3 Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 253 4 ratings Time: 01:20:51 More in People & Blogs

  • Machine Learning course- Shai Ben-David: Lecture 4

    Machine Learning course- Shai Ben-David: Lecture 4 CS 485/685, University of Waterloo. Jan 16, 2015. Extensions of the definition of PAC learnability to various more realistic scenarios. The basic notion of representative samples and how it… From: Understanding Machine Learning – Shai…

  • Machine Learning course- Shai Ben-David: Lecture 2

    Machine Learning course- Shai Ben-David: Lecture 2 CS 485/685, University of Waterloo. Jan 9, 2015. First formal learnability theorem: Assuming realizability, ERM is guaranteed to succeed over finite classes. From: Understanding Machine Learning – Shai Ben-David Views: 120 1 ratings Time: …

  • Machine Learning course- Shai Ben-David: Lecture 3

    Machine Learning course- Shai Ben-David: Lecture 3 CS 485/685, University of Waterloo. Jan 14, 2015. The basic definitions of learnability of a class H: PAC learnability and Agnostic PAC learnability. From: Understanding Machine Learning – Shai Ben-David Views: 106 0 ratings Time: 01:17:59 …

  • Machine Learning course- Shai Ben-David: Lecture 1

    Machine Learning course- Shai Ben-David: Lecture 1 CS 485/685, University of Waterloo. Jan 7, 2015. Introduction: What is machine learning? and an outline of the course. The first 8 minutes are just course administrivia – can be harmlessly skipped. From: Understanding Machine Learning – Shai…

  • Machine Learning, University of Waterloo, Lecture 3

    Machine Learning, University of Waterloo, Lecture 3 Machine Learning course, CS 485/685. Lecture 3: Jan 14, 2015. Instructor: Prof. Shai Ben-David University of Waterloo. From: Samira Samadi Views: 40 1 ratings Time: 01:17:59 More in People & Blogs

  • Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 4 (end)

    Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 4 (end) From: VienNCCaoCap Toan Views: 15 0 ratings Time: 00:16 More in Science & Technology

  • Machine Learning, University of Waterloo, Lecture 2

    Machine Learning, University of Waterloo, Lecture 2 Machine Learning course, CS 485/685. Lecture 2: Jan 9, 2015. Instructor: Prof. Shai Ben-David University of Waterloo. From: Samira Samadi Views: 45 0 ratings Time: 01:17:51 More in People & Blogs

  • Machine Learning, University of Waterloo, Lecture 1

    Machine Learning, University of Waterloo, Lecture 1 Machine Learning course, CS 485/685. Lecture 1: Jan 7, 2015. Instructor: Prof. Shai Ben-David University of Waterloo. From: Samira Samadi Views: 118 1 ratings Time: 01:18:12 More in People & Blogs

  • Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 3

    Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 3 From: VienNCCaoCap Toan Views: 26 1 ratings Time: 30:31 More in Science & Technology

  • Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 2

    Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 2 From: VienNCCaoCap Toan Views: 48 1 ratings Time: 30:32 More in Science & Technology

  • Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 1

    Public lecture “Machine learning for the Internet of Things: Making Smart Things Smart” Part 1 From: VienNCCaoCap Toan Views: 63 2 ratings Time: 30:28 More in Science & Technology

  • 10-601 Machine Learning Spring 2015 – Lecture 1

    10-601 Machine Learning Spring 2015 – Lecture 1 Topics: high-level overview of machine learning, course logistics, decision trees Lecturer: Tom Mitchell. From: Abulhair Saparov Views: 664 5 ratings Time: 01:19:08 More in People & Blogs

  • 10-701 Machine Learning Fall 2014 – Lecture 22

    10-701 Machine Learning Fall 2014 – Lecture 22 Topics: principal component analysis (PCA), deep learning/networks, neural networks Lecturers: Aarti Singh and Geoff Gordon. From: Abulhair Saparov Views: 238 3 ratings Time: 01:15:19 More in People & Blogs

  • Data Science Lecture Series: Maximizing Human Potential Using Machine Learning-Driven Applications

    Data Science Lecture Series: Maximizing Human Potential Using Machine Learning-Driven Applications Data Science Lecture Series: Maximizing Human Potential Using Machine Learning-Driven Applications Lecture | September 19 | 1:00-2:30 p.m. | Sutardja Dai Hall, Banatao Auditorium … From: Berkeley…

  • 10-701 Machine Learning Fall 2014 – Lecture 20

    10-701 Machine Learning Fall 2014 – Lecture 20 Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Lecturer: Aarti Singh. From: Abulhair Saparov Views: 235 1 ratings Time: 01:14:49 More in People & Blogs

  • Machine Learning and Big Data in Cyber Security Eyal Kolman Technion lecture

    Machine Learning and Big Data in Cyber Security Eyal Kolman Technion lecture Machine Learning and Big Data in Cyber Security – lecture by yal Kolman of RSA given at Technion-Israel Institute of Technoloy, Technion Computer Engineering summer school 2014 Although … From: Technion Views: 533 …

  • 10-701 Machine Learning Fall 2014 – Lecture 19

    10-701 Machine Learning Fall 2014 – Lecture 19 Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity Lecturer: Aarti Singh. From: Abulhair Saparov Views: 352 2 ratings Time: 01:10:26 More in People & Blogs

  • 10-701 Machine Learning Fall 2014 – Lecture 14

    10-701 Machine Learning Fall 2014 – Lecture 14 Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff Gordon. From: Abulhair Saparov Views: 417 1 ratings Time: 01:14:42 More in People & Blogs

  • 10-701 Machine Learning Fall 2014 – Lecture 11

    10-701 Machine Learning Fall 2014 – Lecture 11 Topics: Newton’s method, backtracking line search, constrained optimization, stochastic gradient descent, density estimation Lecturers: Geoff Gordon and Aarti Singh. From: Abulhair Saparov Views: 501 2 ratings Time: 01:19:56 More in People…

  • 10-701 Machine Learning Fall 2014 – Lecture 4

    10-701 Machine Learning Fall 2014 – Lecture 4 Topics: logistic regression, generative vs discriminative classifiers, analysis of perceptron algorithm Lecturers: Aarti Singh and Geoff Gordon. From: Abulhair Saparov Views: 728 1 ratings Time: 01:14:46 More in People & Blogs

  • 10-701 Machine Learning Fall 2014 – Lecture 1

    10-701 Machine Learning Fall 2014 – Lecture 1 Topics: course logistics, high-level overview of machine learning, classification Lecturer: Aarti Singh. From: Abulhair Saparov Views: 1874 0 ratings Time: 01:15:42 More in People & Blogs

  • EE596B Lecture 2, Submodular Functions, Optimization, and Applications to Machine Learning

    EE596B Lecture 2, Submodular Functions, Optimization, and Applications to Machine Learning EE596B Submodular Functions, Optimization, and Applications to Machine Learning Lecture 2 April 2nd, 2014 Prof. Jeff Bilmes http://j.ee.washington.edu/~bilmes/classes/ee596b_spring_2014/ From: Jeffrey A.…

  • Lecture 10: Machine Learning 1

    Lecture 10: Machine Learning 1 Lecture 10: Machine Learning 1 This is a lecture video for the Carnegie Mellon course: ‘Graduate Artificial Intelligence’, Spring 2014. Information about the course is available at http://www.c… From: Zico Kolter Views: 322 0 ratings Time: 01:20:14 …

  • Machine Learning for Computer Vision – Lecture 11 (Dr. Rudolph Triebel)

    Machine Learning for Computer Vision – Lecture 11 (Dr. Rudolph Triebel) Lecturer: Dr. Rudolph Triebel (TU München) Topics covered: – Clustering methods – Dirichlet Process Mixture Models – Affinitiy Propagation – Spectral Clustering – Evaluation Methods. From: cvprtum Views: 682 2 ratings…

  • Machine Learning for Computer Vision – Lecture 9 (Dr. Rudolph Triebel)

    Machine Learning for Computer Vision – Lecture 9 (Dr. Rudolph Triebel) Lecturer: Dr. Rudolph Triebel (TU München) Topics covered: – Sampling Methods – Rejection sampling – Importance sampling – Particle Filter. From: cvprtum Views: 538 2 ratings Time: 01:20:45 More in Science &…

  • Machine Learning for Computer Vision – Lecture 8 (Dr. Rudolph Triebel)

    Machine Learning for Computer Vision – Lecture 8 (Dr. Rudolph Triebel) Lecturer: Dr. Rudolph Triebel (TU München) Topics covered: – Variational Inference – Conjugate Priors – Expectation Propagation. From: cvprtum Views: 1121 2 ratings Time: 01:33:51 More in Science & Technology…

  • Machine Learning for Computer Vision – Lecture 5 (Dr. Rudolph Triebel)

    Machine Learning for Computer Vision – Lecture 5 (Dr. Rudolph Triebel) Lecturer: Dr. Rudolph Triebel (TU München) Topics covered: – Kernel Methods in general – Mercer Kernels – Kernel PCA – Support Vector Machines – From: cvprtum Views: 1152 6 ratings Time: 01:27:25 More in Science &…

  • 10-701 Machine Learning Fall 2013 Lecture 23

    10-701 Machine Learning Fall 2013 Lecture 23 Boosting; HMMs and DBNs; overview of MCMC. From: Geoff Gordon Views: 150 1 ratings Time: 01:15:00 More in Education

  • 10-701 Machine Learning Fall 2013 lecture 19

    10-701 Machine Learning Fall 2013 lecture 19 graphical models: factor graphs, Markov random fields, junction trees Note: interesting part starts at minute 4:30 due to slight technical difficulties at the beginning. From: Geoff Gordon Views: 331 1 ratings Time: 01:18:31 More in Education…

  • Lecture 21: Machine Learning – Perceptrons

    Lecture 21: Machine Learning – Perceptrons CS188 Artificial Intelligence, Fall 2013 Instructor: Prof. Dan Klein. From: CS188Fall2013 Views: 4382 0 ratings Time: 01:14:19 More in Education

  • Lecture 20: Machine Learning – Naive Bayes’

    Lecture 20: Machine Learning – Naive Bayes’ CS188 Artificial Intelligence, Fall 2013 Instructor: Prof. Dan Klein. From: CS188Fall2013 Views: 3066 0 ratings Time: 01:14:46 More in Education

  • Machine Learning 10-701 fall 2013 lecture 17

    Machine Learning 10-701 fall 2013 lecture 17 The bootstrap. From: Geoff Gordon Views: 227 1 ratings Time: 01:17:00 More in Education

  • Machine Learning for Computer Vision – Lecture 1 (Dr. Rudolph Triebel)

    Machine Learning for Computer Vision – Lecture 1 (Dr. Rudolph Triebel) Lecturer: Dr. Rudolph Triebel (TU München) Topics covered: – Linear Regression, General Formulation – Polynomial, Gaussian, Sigmoidal Basis Functions – Overfitting and Regularization – Maximim-li… From: cvprtum Views:…

  • Machine Learning 10-701 Lecture 15, Convergence bounds

    Machine Learning 10-701 Lecture 15, Convergence bounds Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701x/ Law of large numbers, central limit theorem, convergence rates, characteristic function, Slutsky’s theorem. From: Alex Smola Views: 409 1…

  • Machine Learning 10-701 Lecture 13 Novelty Detection

    Machine Learning 10-701 Lecture 13 Novelty Detection Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701x/ From: Alex Smola Views: 731 1 ratings Time: 01:22:16 More in Science & Technology

  • Machine Learning 10-701 Lecture 11 – Lagrange Multipliers and SVMs

    Machine Learning 10-701 Lecture 11 – Lagrange Multipliers and SVMs Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701x/ Optimization, Support Vector Machines. From: Alex Smola Views: 765 1 ratings Time: 01:21:45 More in Science & Technology…

  • Machine Learning 10-701x Lecture 7 Convexity and Optimization

    Machine Learning 10-701x Lecture 7 Convexity and Optimization Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701x/ Geoff Gordon and Alex Smola Introduction to basic optimization and convexity. From: Alex Smola Views: 917 2 ratings Time: 01:14:15 More…

  • Machine Learning 10-701x Lecture 6 Perceptron and Kernels

    Machine Learning 10-701x Lecture 6 Perceptron and Kernels Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ Alex Smola – Perceptrons, kernels, Mercer’s theorem, feature spaces. From: Alex Smola Views: 787 2 ratings Time: 01:20:54 More in …

  • Machine Learning 10-701x Lecture 5

    Machine Learning 10-701x Lecture 5 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ Geoff Gordon – Perceptron. From: Alex Smola Views: 702 5 ratings Time: 01:18:00 More in Science & Technology

  • Machine Learning 10-701x Lecture 3

    Machine Learning 10-701x Lecture 3 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701x/ Density estimation. Basic probability and Bayes Rule featuring Geoff Gordon and Alex Smola. From: Alex Smola Views: 1151 4 ratings Time: 01:17:58 More in Science…

  • Machine Learning 10-701 2013/H2 Lecture 2

    Machine Learning 10-701 2013/H2 Lecture 2 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701x/ Linear Regression, Density estimation, Parzen Windows, Watson Nadaraya Estimator. From: Alex Smola Views: 2489 9 ratings Time: 01:19:00 More in Science…

  • Machine Learning 10-701 2013/H2 Lecture 1

    Machine Learning 10-701 2013/H2 Lecture 1 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701x/ Basic overview of Machine Learning Techniques / Problems / Applications. From: Alex Smola Views: 6907 20 ratings Time: 01:15:43 More in Science &…

  • Lecture 1 (part 3): Introduction to Probabilistic Modelling and Machine Learning

    Lecture 1 (part 3): Introduction to Probabilistic Modelling and Machine Learning Machine Learning and Nonparametric Bayesian Statistics by prof. Zoubin Ghahramani. These lectures are part of the Visiting Professor Programme co-financed by the European Union within … From: Styk Telewizja…

  • Lecture 1 (part 2): Introduction to Probabilistic Modelling and Machine Learning

    Lecture 1 (part 2): Introduction to Probabilistic Modelling and Machine Learning Machine Learning and Nonparametric Bayesian Statistics by prof. Zoubin Ghahramani. These lectures are part of the Visiting Professor Programme co-financed by the European Union within … From: Styk Telewizja…

  • Lecture 1 (part 1): Introduction to Probabilistic Modelling and Machine Learning

    Lecture 1 (part 1): Introduction to Probabilistic Modelling and Machine Learning Machine Learning and Nonparametric Bayesian Statistics by prof. Zoubin Ghahramani. These lectures are part of the Visiting Professor Programme co-financed by the European Union within … From: Styk Telewizja…

  • Andrew Ng – Machine Learning via Large-scale Brain Simulations – Technion lecture

    Andrew Ng – Machine Learning via Large-scale Brain Simulations – Technion lecture Andrew Ng of Stanford University, Technion lecture: Machine Learning via Large-scale Brain Simulations Machine learning is a very successful technology, but applying it to a new problem usually… From: Technion …

  • Machine Learning 10-701 Lecture 17 Directed Graphical Models

    Machine Learning 10-701 Lecture 17 Directed Graphical Models Directed Graphical Models Bayes Ball Algorithm Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ From: Alex Smola Views: 814 2 ratings Time: 01:18:53 More in Science & Technology…

  • Machine Learning 10-701 Lecture 13

    Machine Learning 10-701 Lecture 13 Gaussian Processes (Classification and Regression) Exponential Families (brief intro) Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ From: Alex Smola Views: 932 1 ratings Time: 01:24:21 More in Science &…

  • Machine Learning 10-701 Lecture 12

    Machine Learning 10-701 Lecture 12 Gaussian Processes, Part 1 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ From: Alex Smola Views: 810 1 ratings Time: 01:18:40 More in Science & Technology

  • Machine Learning 10-701 Lecture 7 (Kernel Methods)

    Machine Learning 10-701 Lecture 7 (Kernel Methods) Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ From: Alex Smola Views: 3650 10 ratings Time: 01:20:00 More in Science & Technology

  • Machine Learning 10-701 Lecture 5

    Machine Learning 10-701 Lecture 5 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ From: Alex Smola Views: 1584 3 ratings Time: 01:19:22 More in Science & Technology

  • Machine Learning 10-701 Lecture 4 (improved audio)

    Machine Learning 10-701 Lecture 4 (improved audio) Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ Apologies for the appalling audio. We had a 60Hz loop when using the audio pickup from the lecture… From: Alex Smola Views: 1575 0 ratings Time: …

  • Machine Learning 10-701 Lecture 3

    Machine Learning 10-701 Lecture 3 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ Parzen Windows – kernels, algorithm Model selection – crossvalidation, leave one out, bias variance… From: Alex Smola Views: 2738 7 ratings Time: 01:19:51 More in …

  • Machine Learning 10-701 Lecture 1

    Machine Learning 10-701 Lecture 1 Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-10-701/ Machine Learning Problems Data Applications Basic tools. From: Alex Smola Views: 13865 51 ratings Time: 01:19:49 More in Science & Technology

  • soft computing lecture – hour 3: Machine learning (cont) and Graph Search Methods

    soft computing lecture – hour 3: Machine learning (cont) and Graph Search Methods video lecture series covering theoretical and application areas of soft computing was recorded at ABV-IIITM Gwalior. complete index, video and download link available at http://rkala.in/softcomputi… From: rahul…

  • soft computing lecture – hour 2: Expert Systems, Machine Learning and Pattern Matching

    soft computing lecture – hour 2: Expert Systems, Machine Learning and Pattern Matching video lecture series covering theoretical and application areas of soft computing was recorded at ABV-IIITM Gwalior. complete index, video and download link available at http://rkala.in/softcomputi… From: …

  • Machine Learning Lecture 3: working with text + nearest neighbor classification

    Machine Learning Lecture 3: working with text + nearest neighbor classification We continue our work with sentiment analysis from Lecture 2. I go over common ways of preprocessing text in Machine Learning: n-grams, stemming, stop words, wordnet, and part of speech tagging…. From: MLexplained …

  • Machine Learning Lecture 2: Sentiment Analysis (text classification)

    Machine Learning Lecture 2: Sentiment Analysis (text classification) In this video we work on an actual sentiment analysis dataset (which is an instance of text classification), for which I also provide Python code (see below). The approach is very similar to… From: MLexplained Views: 18003 …

  • Machine Learning Lecture 1: Introduction

    Machine Learning Lecture 1: Introduction This will be a series of lectures on the subject of Machine Learning. The target audience are people who are not in this area of research, but are interested in the subject. Machine Learning… From: MLexplained Views: 6701 68 ratings Time: 07:43 …

  • Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-4/4

    Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-4/4 SANJEEV SHARMA, 12th Nov 2010: Machine Learning: Lecture-11: Kernel Perceptron Learning. CONTENTS: Simple Perceptron Algorithm, Voted Perceptron Algorithm, Kenrel Perceptron Algorithm…. From: Sanjeev Sharma Views:…

  • Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-3/4

    Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-3/4 SANJEEV SHARMA, 12th Nov 2010: Machine Learning: Lecture-11: Kernel Perceptron Learning. CONTENTS: Simple Perceptron Algorithm, Voted Perceptron Algorithm, Kenrel Perceptron Algorithm…. From: Sanjeev Sharma Views:…

  • Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-2/4

    Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-2/4 SANJEEV SHARMA, 12th Nov 2010: Machine Learning: Lecture-11: Kernel Perceptron Learning. CONTENTS: Simple Perceptron Algorithm, Voted Perceptron Algorithm, Kenrel Perceptron Algorithm…. From: Sanjeev Sharma Views:…

  • Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-1/4

    Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-1/4 SANJEEV SHARMA, 12th Nov 2010: Machine Learning: Lecture-11: Kernel Perceptron Learning. CONTENTS: Simple Perceptron Algorithm, Voted Perceptron Algorithm, Kenrel Perceptron Algorithm…. From: Sanjeev Sharma Views:…

  • Lecture-10: Machine Learning: Kullback-Leibler Divergence & Convex Analysis Part-3.mp4

    Lecture-10: Machine Learning: Kullback-Leibler Divergence & Convex Analysis Part-3.mp4 MACHINE LEARNING: Lecture-10: Kullback-Leibler Divergence & Convex Analysis. Sanjeev Sharma, IIT Roorkee, Undergraduate, Founder & Co-Owner – searching-eye.com CONTENTS: Convex … From: Sanjeev…

  • Lecture-10: Machine Learning: Kullback-Leibler Divergence & Convex Analysis Part-2.mp4

    Lecture-10: Machine Learning: Kullback-Leibler Divergence & Convex Analysis Part-2.mp4 MACHINE LEARNING: Lecture-10: Kullback-Leibler Divergence & Convex Analysis. Sanjeev Sharma, IIT Roorkee, Undergraduate, Founder & Co-Owner – searching-eye.com CONTENTS: Convex … From: Sanjeev…

  • Lecture-10: Machine Learning: Kullback-Leibler Divergence & Convex Analysis Part-1.mp4

    Lecture-10: Machine Learning: Kullback-Leibler Divergence & Convex Analysis Part-1.mp4 MACHINE LEARNING: Lecture-10: Kullback-Leibler Divergence & Convex Analysis. Sanjeev Sharma, IIT Roorkee, Undergraduate, Founder & Co-Owner – searching-eye.com CONTENTS: Convex … From: Sanjeev…

  • Lecture 2: Sanjeev Sharma: Locally Weighted Regression: Machine Learning

    Lecture 2: Sanjeev Sharma: Locally Weighted Regression: Machine Learning Sanjeev Sharma: Btech, 3rd year, 5th semester student of IIT Roorkee, Electrical Engineering. This is my own lecture. 2nd video in Machine Learning Section for my website searching-eye.com… From: Sanjeev Sharma Views:…