DS 学习 - 打卡贴【欢迎指教】

感谢楼上几位的鼓励，从今天起恢复正常学习。

6/10-6/22
www.kaggle.com  Bag of Words Meets Bags of Popcorn
sentiment analysis, word2vec, clustering

6/23-7/16
Probabilistic Graphical Models 1 at coursera
week1 Bayesian Network fundamentals, independencies
week2 Template models and structured CPDs for BN

7/17-7/27
Probabilistic Graphical Models 1 at coursera
week 2 Programming Assignment: BNs for Genetic Inheritance
week 3 Markov Network Fundamentals, independencies, I-maps, log-linear models
           Programming Assignment: Markov Networks for OCR

7/28-8/5
Probabilistic Graphical Models 1 at coursera
week 4 Decision theory, Maximum expected utility, Utility functions, Value of perfect information
           Programming Assignment - Decision making

read ch1-2, ch3.1-3.2 in book "Probabilistic graphical models - principles and techniques" by Daphne Koller, Nir Niredman

8/6-8/13
Probabilistic Graphical Models 1 at coursera
week 5 Knowledge Engineering, Final Exam

Probabilistic Graphical Models 2 at coursera
week 1 Variable Elimination

read ch3.3-3.4 in book "Probabilistic graphical models - principles and techniques" by Daphne Koller, Nir Niredman

8/14-8/20
Probabilistic Graphical Models 2 at coursera
week 2 Belief Passing in Cluster Graph, Clique Tree Algorithm, VE
programming assignment: Exact Inference

Leetcode 126, 128

8/21-8/28
Probabilistic Graphical Models 2 at coursera
week 3 MAP message passing, Dual decomposition
week 4 Sampling, MCMC, Gibbs Sampling, Metropolis Hastings Algorithm, Inference in Temporal Models
programming assignment: Sampling Methods

8/29-9/4
Probabilistic Graphical Models 2 at coursera
review and finished final exam

LeetCode: 132, 135, 140, 145