Pattern Recognition and Machine Learning / 模式识别与机器学习
- Lecture: Course information and grading policy; AI; optimization; machine learning.
- Lab: Environment configuration
- Lecture: Pattern recognition; curve fitting; probabilities.
- Lab: Adult Census Income Prediction
- Assignment Ⅰ
- Written assignment 01
- Program assignment: curve fitting
- Lecture: Inference; decision; entropy and information.
- Lab: Naive-Bayes-based Spam Email Classification
- Lecture: Binomial distributions; multinomial distributions; Gaussian distributions.
- Lab: Linear Regression-based California Housing Analysis
- Lecture: Gaussian distributions; exponential family.
- Lab: Decision Tree/Ensemble Learning-based Iris Classification
- Assignment Ⅱ
- Written assignment 02
- Program assignment: KNN breast cancer prediction
- Lecture: KNN; Linear basis; Maximum Likelihood and Least Squares; Bias Variance Decomposition; Bayesian linear regression; predictive distribution; maximum evidence.
- Lab: LDA-based Handwritten Number Recognition
- Quiz 1
- Lecture: Linear Classification; Discriminant Function; Fisher.
- Lab: MLP-based Handwritten Number Recognition(optional)
- Lecture: Perceptrons; generative Gaussian models; Bayesian Gaussian models; logistic regression; Bayesian logistic regression.
- Lab: CNN-based Image Classifier for CIFAR10 Dataset
- Assignment Ⅲ
- Written assignment 03
- Program Assignment: Multi-class Logistic Regression
- Quiz 2
- Lecture: Feedforward network; network training; BP; Jacobian and Hessian; regularization.
- Lab: Object Detection and Tracking
- Lecture: Bayesian neural networks; CNN and GAN.
- Lab: Vehicle Detection with HOG & SVM
- Assignment Ⅳ
- Written assignment 04
- Program Assignment: CNN for MNIST Classification
- Quiz 3
- Lecture: Support Vector Machines.
- Lab: K-means Clustering-based Object Segmentation for Videos(optional)
- Lecture: SVM for classification and regression; RVM for classification and regression.
- Lab: Gaussian Mixture Model-based Object Segmentation(optional)
- Project Proposal
- Midterm Exam (take-home)
- Lecture: K-means; GMM; EM.
- Lab: MDP-based Shortest Path Solver with Collision Avoidance
- Lecture: HMM;EM for HMM; forward-backward.
- Lab: Q-learning with taxi v3🚕
- Assignment Ⅴ
- Written assignment 05
- Quiz 4
- Lecture: Dynamic programming; MDP.
- Lab: RNN-based Name Classification
- Lecture: MDP; value iteration; policy iteration.
- Lab: Review
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