Lecture 10: Linear Regression III Geometric Interpretation

Nipun Batra Keynote ACM Compass 2024 Doctoral Consortium

Lecture 31: Overall revision (quick walk through of previous 30 lectures)

Lecture 30: Soft margin SVM

Lecture 29 and tutorial: SVM solution via QP; Kernels as measure of similarity

Lecture 28 + Tutorial: SVM II (Kernel trick)

Lecture 27: Support Vector Machines I

Lecture 26 +. Tutorial: Approximate KNN

Lecture 25: Unsupervised Learning

Lecture 24: Reinforcement Learning

Lecture 23: K Nearest Neighbors (and the Curse of Dimensionality)

Lecture 22: Convolutional Neural Network II

Lecture 21: Convolutional Neural Network I

Lecture 20: Autograd

Lecture 19: Next token prediction using MLPs

Lecture 18: Neural Networks II (Guest lecture Zeel B Patel)

Lecture 17: Neural Networks I

Lecture 16: Logistic Regression II

Lecture 15: Logistic Regression I

Lecture 14: Regularized linear regression

Lecture 13, Tutorial 5: Matrix Factorisation, Movie Recommendation

Lecture 12: Stochastic Gradient Descent

Lecture 11 + Tutorial 4: Gradient Descent I

Lecture 10: Linear Regression III Dummy Variables and Multi-colinearity

Lecture 10: Linear Regression III Geometric Interpretation