2025 Lecture Slides

Machine Learning — 2025 Materials
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01 Machine Learning and Statistical Learning.pdfDownload
02 Perceptron.pdfDownload
03 k-Nearest Neighbors.pdfDownload
04 Bayesian Classifiers.pdfDownload
06 Logistic Regression and Maximum Entropy.pdfDownload
07 Support Vector Machines.pdfDownload
09 Expectation-Maximization and Extensions.pdfDownload
13 Introduction to Unsupervised Learning.pdfDownload
14 Clustering Methods.pdfDownload
15 Singular Value Decomposition.pdfDownload
16 Principal Component Analysis.pdfDownload
Appendix-Distribution-Measures.pdfDownload
DL/01 Overview Linear Algebra and NDArray.pdfDownload
DL/03 Derivatives Backpropagation and Complexity.pdfDownload
DL/04 Linear Methods Basic Optimization and Linear Regression.pdfDownload
DL/05 Maximum Likelihood Estimation and Logistic Regression.pdfDownload
DL/06 Multilayer Perceptrons.pdfDownload
DL/08 Numerical Stability Activation Functions and Hardware.pdfDownload
DL/11 Convolution and Pooling Layers.pdfDownload
DL/12 LeNet AlexNet VGG and NiN.pdfDownload
DL/13 Inception Batch Normalization and Residual Networks.pdfDownload
DL/15 Image Augmentation Fine-Tuning and Style Transfer.pdfDownload
DL/16 Object Detection and Computer Vision Training Techniques.pdfDownload
DL/18 Sequence Models.pdfDownload
DL/19 Recurrent Neural Networks.pdfDownload
DL/20 Advanced Recurrent Neural Networks.pdfDownload
DL/24 Attention Mechanisms.pdfDownload
DL/24 ViT.pdfDownload
DL/25 Optimization Problems.pdfDownload
DL/DM/00 Prerequisites.pdfDownload
DL/DM/01 AE.pdfDownload
DL/DM/02 EM.pdfDownload
DL/DM/03 VAE.pdfDownload
DL/DM/04 GAN.pdfDownload
DL/DM/05 Diffusion Model.pdfDownload
DL/LM/00 Language Model Overview.pdfDownload
DL/LM/01 Text Preprocessing.pdfDownload
DL/LM/02 Statistical Language Models.pdfDownload
DL/LM/03 Word Embeddings.pdfDownload
DL/LM/04 Sequence-to-Sequence Models.pdfDownload
DL/LM/05 Pretrained Models.pdfDownload
Linear-Algebra-Fundamentals-ML.pdfDownload