lectures
27 items under this folder.
- Lecture 1: Course Overview and Probability
- Lecture 2: Probability
- Lecture 3: Linear Algebra and PCA
- Lecture 4: Logic
- Lecture 5: Natural Language Processing
- Lecture 6: Machine Learning Overview
- Lecture 7: Unsupervised Learning I
- Lecture 8: Unsupervised Learning II
- Lecture 9: Linear Models & Linear Regression
- Lecture 10: Classification - KNN and Naive Bayes
- Lecture 11: Perceptron
- Lecture 12: Neural Networks II
- Lecture 13: Neural Networks III
- Lecture 14: Midterm Review
- Lecture 15: Deep Learning I - Convolutional Neural Networks
- Lecture 16: Convolutional Neural Networks (II)
- Lecture 17: Deep Learning III
- Lecture 18: Deep Learning IV
- Lecture 19: Uninformed Search
- Lecture 20: Informed Search
- Lecture 21: Games I
- Lecture 22: Games II
- Lecture 23: Reinforcement Learning I
- Lecture 24: Reinforcement Learning II
- Lecture 25: Advanced Search (Optimization)
- Lecture 26: Ethics and Trust in AI
- Lecture 27: Review