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ML Curriculum Before & After AI Winter
- 1. Lead Kwanghee Choi Team LearningMachine Microsoft Student Partners
- 2. Lead Kwanghee Choi Team LearningMachine Microsoft Student Partners
- 3. Copyright Coursera | Machine Learning by Andrew Ng
- 4. Copyright Coursera | Machine Learning by Andrew Ng
- 5. Copyright Coursera | Machine Learning by Andrew Ng
- 6. Copyright Coursera | Machine Learning by Andrew Ng
- 7. Copyright Coursera | Machine Learning by Andrew Ng
- 8. Copyright Coursera | Machine Learning by Andrew Ng
- 9. Copyright Coursera | Machine Learning by Andrew Ng
- 10. Copyright Coursera | Machine Learning by Andrew Ng
- 11. Copyright Coursera | Machine Learning by Andrew Ng
- 12. Copyright Coursera | Machine Learning by Andrew Ng
- 31. Lead Kwanghee Choi Team LearningMachine Microsoft Student Partners
- 32. Lead Kwanghee Choi Team LearningMachine Microsoft Student Partners
- 33. Lead Kwanghee Choi Team LearningMachine Microsoft Student Partners
- 34. Lead Kwanghee Choi Team LearningMachine Microsoft Student Partners
- 35. Collaboration with Sogang Univ. Release Promotions & Venue supported
- 36. Collaboration with Sogang Univ. Release Promotions & Venue supported
- 37. Collaboration with Sogang Univ. Release Promotions & Venue supported
- 38. Collaboration with Sogang Univ. Release Promotions & Venue supported
- 39. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 40. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 41. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 42. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 43. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 44. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 45. Theory Lectures Before & After AI Winter
- 46. Theory Lectures Before & After AI Winter
- 47. CNTK in practice Before & After AI Winter
- 48. CNTK in practice Before & After AI Winter
- 49. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 50. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 51. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 52. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 53. Phase1 Theory Lecture • Definition of ML • Issues in ML • ML Applications - Regression - Binary Classification - Multi-label Classification • ML Algorithms - Linear & Logistic Regression - Activation Function: Sigmoid - Softmax Classifier Phase2 Theory Lecture • Real-world Issues in ML - Overfitting - Regularization - Evaluation • Rise and Fall of AI: The AI Winter - Perceptrons - Back Propagation - Vanishing Gradients - Activation Function: ReLU - Restricted Boltzmann Machine • Recent Breakthroughs CNTK in practice • Environment Setup • Logistic Regression • Softmax Classifier • MNIST Dataset CNTK in practice • MNIST with Deep NN - Train, Test & Evaluation - Stochastic Gradient Descent Homework • MNIST Multilayer Softmax Classifier Hackathon • Dataset Preparation • Image Preprocessing • Binary Image Classification Curriculum Before & After AI Winter
- 54. The AI Winter Hackathon: PotatoHunter Normal Potato v. Sweet Potato
- 55. The AI Winter Hackathon: PotatoHunter Normal Potato v. Sweet Potato
- 56. The AI Winter Hackathon: PotatoHunter Normal Potato v. Sweet Potato
- 57. The AI Winter Hackathon: PotatoHunter Normal Potato v. Sweet Potato
- 58. The AI Winter Hackathon: PotatoHunter Normal Potato v. Sweet Potato
- 59. The AI Winter Hackathon: PotatoHunter Normal Potato v. Sweet Potato
- 60. The AI Winter Hackathon: PotatoHunter @ Sinchon Dreamtop Cafe
- 61. The AI Winter Hackathon: PotatoHunter @ Sinchon Dreamtop Cafe
- 62. The AI Winter Hackathon: PotatoHunter @ Sinchon Dreamtop Cafe
- 63. The AI Winter Hackathon: PotatoHunter Results 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Given Example 1st Place Winner Train Error (200 Images) Test Error (20 Images)
- 64. The AI Winter Hackathon: PotatoHunter Results 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Given Example 1st Place Winner Train Error (200 Images) Test Error (20 Images)
- 65. The AI Winter Hackathon: PotatoHunter Results 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Given Example 1st Place Winner Train Error (200 Images) Test Error (20 Images)
- 66. The AI Winter Hackathon: PotatoHunter Results 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Given Example 1st Place Winner Train Error (200 Images) Test Error (20 Images)
- 67. The AI Winter Hackathon: PotatoHunter Results 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Given Example 1st Place Winner Train Error (200 Images) Test Error (20 Images)
- 68. The AI Winter Hackathon: PotatoHunter Results 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Given Example 1st Place Winner Train Error (200 Images) Test Error (20 Images)