├── .gitattributes ├── .gitignore ├── Bayesian_Statistics_From_Concept_to_Data_Analysis_UC_Santa_Cruz ├── Lesson 1.pdf ├── Lesson 10.pdf ├── Lesson 11.pdf ├── Lesson 2.pdf ├── Lesson 3.1.pdf ├── Lesson 3.2-3.3.pdf ├── Lesson 4.pdf ├── Lesson 5.1-5.2.pdf ├── Lesson 5.3-5.4.pdf ├── Lesson 6.pdf ├── Lesson 7.pdf ├── Lesson 8.pdf ├── Lesson 9.pdf ├── Module 1 Honors.pdf ├── Module 2 Honors.pdf ├── Module 3 Honors.pdf ├── Module 4 Honors.pdf └── Regression.pdf ├── Learn_to_Program_Crafting_Quality_Code_University_of_Toronto ├── Rat Race │ ├── a2.py │ ├── maze.txt │ └── rat_race.py ├── Week 1 Exercise.pdf ├── Week 2 Exercise.pdf ├── Week 3 Exercise.pdf ├── Week 4 Exercise.pdf └── Writing test cases │ ├── a1.py │ ├── test_num_buses.py │ ├── test_stock_price_summary.py │ └── test_swap_k.py ├── Neural_Networks_for_Machine_Learning_University_of_Toronto ├── Assignment1 │ ├── Datasets │ │ ├── dataset1.mat │ │ ├── dataset1_ancient_octave.mat │ │ ├── dataset2.mat │ │ ├── dataset2_ancient_octave.mat │ │ ├── dataset3.mat │ │ ├── dataset3_ancient_octave.mat │ │ ├── dataset4.mat │ │ └── dataset4_ancient_octave.mat │ ├── learn_perceptron.m │ └── plot_perceptron.m ├── Assignment2 │ ├── README.txt │ ├── data.mat │ ├── display_nearest_words.m │ ├── fprop.m │ ├── load_data.m │ ├── predict_next_word.m │ ├── raw_sentences.txt │ ├── train.m │ └── word_distance.m ├── Assignment3 │ ├── a3.m │ └── data.mat ├── Assignment4 │ ├── a4_init.m │ ├── a4_main.m │ ├── a4_rand.m │ ├── a4_randomness_source.mat │ ├── cd1.m │ ├── configuration_goodness.m │ ├── configuration_goodness_gradient.m │ ├── data_set.mat │ ├── describe_matrix.m │ ├── extract_mini_batch.m │ ├── hidden_state_to_visible_probabilities.m │ ├── logistic.m │ ├── optimize.m │ ├── sample_bernoulli.m │ ├── show_rbm.m │ └── visible_state_to_hidden_probabilities.m ├── Final_Exam.pdf ├── Lecture_10_Quiz.pdf ├── Lecture_11_Quiz.pdf ├── Lecture_12_Quiz.pdf ├── Lecture_13_Quiz.pdf ├── Lecture_14_Quiz.pdf ├── Lecture_15_Quiz.pdf ├── Lecture_1_Quiz.pdf ├── Lecture_2_Quiz.pdf ├── Lecture_3_Quiz.pdf ├── Lecture_3_Quiz_1.pdf ├── Lecture_3_Quiz_2.pdf ├── Lecture_4_Quiz.pdf ├── Lecture_5_Quiz.pdf ├── Lecture_6_Quiz.pdf ├── Lecture_7_Quiz.pdf ├── Lecture_8_Quiz.pdf ├── Lecture_9_Quiz.pdf ├── PA_1_The perceptron learning algorithm.pdf ├── PA_2_Learning Word Representations.pdf ├── PA_3_Optimization and generalization.pdf └── PA_4_Restricted Boltzmann Machines.pdf ├── README.md ├── Specialization_Advanced_Machine_Learning_Higher_School_of_Economics ├── Course1_Introduction to Deep Learning │ ├── week1_Introduction to optimization │ │ ├── Linear models.pdf │ │ ├── Overfitting and regularization.pdf │ │ └── week01_pa.ipynb │ ├── week2_Introduction to neural networks │ │ ├── Keras-task.ipynb │ │ ├── Multilayer perceptron.pdf │ │ ├── NumpyNN (honor).ipynb │ │ ├── Tensorflow-task.ipynb │ │ └── my1stNN.ipynb │ ├── week3_Deep Learning for images │ │ ├── Convolutions and pooling.pdf │ │ ├── week3_task1_first_cnn_cifar10_clean.ipynb │ │ └── week3_task2_fine_tuning_clean.ipynb │ ├── week4_Unsupervised representation learning │ │ ├── Adversarial-task.ipynb │ │ ├── Autoencoders-task.ipynb │ │ └── Word embeddings.pdf │ ├── week5_Deep learning for sequences │ │ ├── How to use RNNs.pdf │ │ ├── Modern RNNs(first try).pdf │ │ ├── Modern RNNs.pdf │ │ ├── RNN and Backpropagation.pdf │ │ └── RNN-task.ipynb │ └── week6_Final Project │ │ └── week6_final_project_image_captioning.ipynb └── Course4_Practical Reinforcement Learning │ ├── LICENSE.md │ ├── README.md │ ├── grading.py │ ├── week1_intro │ ├── crossentropy_method.ipynb │ ├── deep_crossentropy_method.ipynb │ ├── gym_interface.ipynb │ ├── primer │ │ ├── recap_ml.ipynb │ │ └── recap_tensorflow.ipynb │ ├── submit.py │ └── xvfb.pid │ ├── week2_model_based │ ├── Optimality in RL.pdf │ ├── Policy Iteration.pdf │ ├── Reward design.pdf │ ├── mdp.py │ ├── practice_vi.ipynb │ └── submit.py │ ├── week3_model_free │ ├── Model-free reinforcement learning.pdf │ ├── experience_replay.ipynb │ ├── qlearning.ipynb │ ├── qlearning.py │ ├── sarsa.ipynb │ └── submit.py │ ├── week4_approx │ ├── DQN.pdf │ ├── MC & TD.pdf │ ├── SARSA and QLeaning.pdf │ ├── atari_util.py │ ├── dqn_atari.ipynb │ ├── framebuffer.py │ ├── img │ │ ├── 1.png │ │ ├── 2.png │ │ ├── cnn.png │ │ ├── exp_rep.png │ │ └── target.png │ ├── practice_approx_qlearning.ipynb │ ├── replay_buffer.py │ └── submit.py │ ├── week5_policy_based │ ├── A policy-based quiz.pdf │ ├── atari_util.py │ ├── img │ │ ├── nnet_arch.png │ │ ├── parallel.png │ │ └── train.gif │ ├── practice_a3c.ipynb │ ├── practice_reinforce.ipynb │ └── submit.py │ ├── week6_outro │ ├── Exploration.pdf │ ├── MCTS.pdf │ ├── bandits.ipynb │ ├── practice_mcts.ipynb │ ├── seq2seq │ │ ├── basic_model_tf.py │ │ ├── he-pron-wiktionary.txt │ │ ├── practice_tf.ipynb │ │ └── voc.py │ └── submit.py │ └── xvfb ├── Specialization_Applied_Data_Science_with_Python_University_of_Michigan ├── Course_1_Introduction_to_Data_Science_in_Python │ ├── Assignment_2.ipynb │ ├── Assignment_3.ipynb │ ├── Assignment_4.ipynb │ └── Week One Quiz.pdf └── Course_3_Applied_Machine_Learning_in_Python │ ├── Assignment 1.ipynb │ ├── Assignment 2.ipynb │ ├── Assignment 3.ipynb │ ├── Assignment 4.ipynb │ ├── Module 1 Quiz.pdf │ ├── Module 2 Quiz.pdf │ ├── Module 3 Quiz.pdf │ └── Module 4 Quiz.pdf ├── Specialization_Big_Data_UC_San_Diego ├── Course2_Big_Data_Modeling_and_Management_Systems │ ├── week2_Practical Quiz for Week 2 Hands-On Lectures.pdf │ ├── week3_Data Models Quiz.pdf │ ├── week4_Data Formats and Streaming Data Quiz.pdf │ └── week5_BDMS Quiz.pdf └── Course3_Big_Data_Integration_and_Processing │ ├── week2_Postgres, MongoDB, and Pandas.pdf │ ├── week2_Retrieving Big Data Quiz.pdf │ ├── week3_Hands-On With Splunk.pdf │ ├── week3_Information Integration - Quiz.pdf │ ├── week4_Pipeline and Tools.pdf │ ├── week4_WordCount in Spark.pdf │ ├── week5_More on Spark.pdf │ ├── week5_SparkSQL and Spark Streaming.pdf │ ├── week6_Check Your Analysis Results.pdf │ └── week6_Check Your Query Results.pdf ├── Specialization_Data_Mining_UIUC ├── Course2_Text_Retrieval_and_Search_Engines │ ├── Week 1 Quiz.pdf │ ├── Week 2 Quiz.pdf │ ├── Week 3 Quiz.pdf │ ├── Week 4 Quiz.pdf │ ├── Week 5 Quiz.pdf │ └── Week 6 Quiz.pdf ├── Course3_Text_Mining_and_Analytics │ ├── Week 1 Quiz.pdf │ ├── Week 2 Quiz.pdf │ ├── Week 3 Quiz.pdf │ ├── Week 4 Quiz.pdf │ ├── Week 5 Quiz.pdf │ └── Week 6 Quiz.pdf ├── Course4_Pattern_Discovery_in_Data_Mining │ ├── week1_Frequent Itemset Mining Using Apriori │ │ ├── categories.txt │ │ └── fpgrowth.py │ ├── week1_Lesson 1 Quiz.pdf │ ├── week1_Lesson 2 Quiz.pdf │ ├── week2_Lesson 3 Quiz.pdf │ ├── week2_Lesson 4 Quiz.pdf │ ├── week3_Lesson 5 Quiz.pdf │ ├── week3_Lesson 6 Quiz.pdf │ ├── week4_Lesson 7 Quiz.pdf │ └── week4_Lesson 8 Quiz.pdf └── Course5_Cluster_Analysis_in_Data_Mining │ ├── Lesson 1 Quiz.pdf │ ├── Lesson 2 Quiz.pdf │ ├── Lesson 3 Quiz.pdf │ ├── Lesson 4 Quiz.pdf │ ├── Lesson 5 Quiz.pdf │ ├── Lesson 6 Quiz.pdf │ ├── week2_Implementing the K-means Clustering Algorithm │ ├── K-means clustering.py │ ├── clusters.txt │ └── places.txt │ └── week4_Implementing Clustering Validation Measures │ ├── Implementing Clustering Validation Measures.py │ ├── clustering_1.txt │ ├── clustering_2.txt │ ├── clustering_3.txt │ ├── clustering_4.txt │ ├── clustering_5.txt │ ├── partitions.txt │ └── scores.txt ├── Specialization_Data_Science_Johns_Hopkins_University └── Course2_R_Programming │ ├── week2_Programming with R │ ├── complete.R │ ├── corr.R │ └── pollutantmean.R │ └── week4_Simulation & Profiling │ ├── best.R │ ├── rankall.R │ └── rankhospital.R ├── Specialization_Data_Structures_Algorithms_UC_San_Diego ├── Course1_Algorithmic_Toolbox │ ├── README.md │ ├── week2_Introduction │ │ ├── fibonacci │ │ │ ├── Fibonacci.java │ │ │ ├── fibonacci.cpp │ │ │ └── fibonacci.py │ │ ├── fibonacci_last_digit │ │ │ ├── FibonacciLastDigit.java │ │ │ ├── fibonacci_last_digit.cpp │ │ │ └── fibonacci_last_digit.py │ │ ├── gcd │ │ │ ├── GCD.java │ │ │ ├── gcd.cpp │ │ │ └── gcd.py │ │ └── lcm │ │ │ ├── LCM.java │ │ │ ├── lcm.cpp │ │ │ └── lcm.py │ ├── week3_Greedy_Algorithms │ │ ├── 02_greedy_algorithms_problems.pdf │ │ ├── README.md │ │ ├── change │ │ │ ├── Change.java │ │ │ ├── by_learners │ │ │ │ └── change.rb │ │ │ ├── change.cpp │ │ │ ├── change.hs │ │ │ └── change.py │ │ ├── covering_segments │ │ │ ├── CoveringSegments.java │ │ │ ├── by_learners │ │ │ │ └── covering_segments.rb │ │ │ ├── covering_segments.cpp │ │ │ ├── covering_segments.hs │ │ │ └── covering_segments.py │ │ ├── different_summands.cpp │ │ ├── different_summands │ │ │ ├── DifferentSummands.java │ │ │ ├── by_learners │ │ │ │ └── different_summands.rb │ │ │ ├── different_summands.cpp │ │ │ ├── different_summands.hs │ │ │ └── different_summands.py │ │ ├── dot_product.cpp │ │ ├── dot_product │ │ │ ├── DotProduct.java │ │ │ ├── by_learners │ │ │ │ └── dot_rpoduct.rb │ │ │ ├── dot_product.cpp │ │ │ ├── dot_product.hs │ │ │ └── dot_product.py │ │ ├── fractional_knapsack │ │ │ ├── FractionalKnapsack.java │ │ │ ├── by_learners │ │ │ │ └── fractional_knapsack.rb │ │ │ ├── fractional_knapsack.cpp │ │ │ ├── fractional_knapsack.hs │ │ │ └── fractional_knapsack.py │ │ ├── largest_number.cpp │ │ └── largest_number │ │ │ ├── LargestNumber.java │ │ │ ├── by_learners │ │ │ └── largest_number.rb │ │ │ ├── largest_number.cpp │ │ │ ├── largest_number.hs │ │ │ └── largest_number.py │ └── week4_Divide_and_Conquer │ │ ├── README.md │ │ ├── inversions.cpp │ │ ├── points_and_segments.cpp │ │ └── sorting.cpp ├── Course2_Data_Structures │ ├── README.md │ ├── week1_Basic_Data_Structures │ │ ├── check_brackets_in_code │ │ │ ├── check_brackets.cpp │ │ │ ├── check_brackets.java │ │ │ └── check_brackets.py │ │ ├── network_packet_processing_simulation │ │ │ ├── process_packages.cpp │ │ │ ├── process_packages.java │ │ │ └── process_packages.py │ │ └── tree_height │ │ │ ├── tree-height.cpp │ │ │ ├── tree-height.py │ │ │ └── tree_height.java │ ├── week2_Priority_Queues_and_Disjoint_Sets │ │ ├── job_queue │ │ │ ├── JobQueue.java │ │ │ ├── job_queue.cpp │ │ │ └── job_queue.py │ │ ├── make_heap │ │ │ ├── BuildHeap.java │ │ │ ├── build_heap.cpp │ │ │ └── build_heap.py │ │ └── merging_tables │ │ │ ├── MergingTables.java │ │ │ ├── merging_tables.cpp │ │ │ └── merging_tables.py │ ├── week3_Hash_Tables │ │ ├── hash_chains │ │ │ ├── HashChains.java │ │ │ ├── hash_chains.cpp │ │ │ └── hash_chains.py │ │ ├── hash_substring │ │ │ ├── HashSubstring.java │ │ │ ├── hash_substring(Rabin-Karp).cpp │ │ │ └── hash_substring.py │ │ └── phone_book │ │ │ ├── PhoneBook.java │ │ │ ├── phone_book.cpp │ │ │ └── phone_book.py │ └── week5_Binary_Search_Trees │ │ ├── set_range_sum.cpp │ │ └── tree_orders │ │ ├── tree-orders.cpp │ │ ├── tree-orders.py │ │ └── tree_orders.java ├── Course3_Algorithms_on_Graphs │ ├── README.md │ ├── week1_Decomposition_of_Graphs_1 │ │ ├── connected_components.cpp │ │ └── reachability.cpp │ ├── week2_Decomposition_of_Graphs_2 │ │ ├── acyclicity.cpp │ │ ├── strongly_connected.cpp │ │ └── toposort.cpp │ ├── week3_Paths_in_Graphs_1 │ │ ├── bfs.cpp │ │ └── bipartite.cpp │ ├── week4_Paths_in_Graphs_2 │ │ ├── dijkstra.cpp │ │ ├── negative_cycle.cpp │ │ └── shortest_paths.cpp │ └── week5_Minimum_Spanning_Trees │ │ ├── clustering.cpp │ │ └── connecting_points.cpp └── Course4_Algorithms_on_Strings │ ├── README.md │ ├── week1_Suffix_Tress │ ├── trie.cpp │ ├── trie_matching.cpp │ └── trie_matching_extended.cpp │ ├── week2_Burrows_Wheeler_Transform_and_Suffix_Arrays │ ├── bwt.cpp │ └── bwtinverse.cpp │ └── week3_Algorithmic_Challenges │ └── kmp.cpp ├── Specialization_Deep_Learning_deeplearning.ai ├── Course1_Neural Networks and Deep Learning │ ├── week1_Introduction to deep learning │ │ └── Introduction to deep learning.pdf │ ├── week2_Neural Networks Basics │ │ ├── Logistic+Regression+with+a+Neural+Network+mindset+v3.ipynb │ │ └── Neural Network Basics.pdf │ ├── week3_Shallow neural networks │ │ ├── Planar+data+classification+with+one+hidden+layer+v3.ipynb │ │ └── Shallow Neural Networks.pdf │ └── week4_Deep Neural Networks │ │ ├── Building+your+Deep+Neural+Network+-+Step+by+Step+v3.ipynb │ │ ├── Deep+Neural+Network+-+Application+v3.ipynb │ │ └── Key concepts on Deep Neural Networks.pdf ├── Course2_Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization │ ├── week1_Practical aspects of Deep Learning │ │ ├── Gradient+Checking.ipynb │ │ ├── Initialization.ipynb │ │ ├── Practical aspects of deep learning.pdf │ │ └── Regularization.ipynb │ ├── week2_Optimization algorithms │ │ ├── Optimization algorithms.pdf │ │ └── Optimization+methods.ipynb │ └── week3_Hyperparameter tuning, Batch Normalization and Programming Frameworks │ │ ├── Hyperparameter tuning.pdf │ │ └── Tensorflow+Tutorial.ipynb ├── Course3_Structuring Machine Learning Projects │ ├── Autonomous driving (case study).pdf │ └── Bird recognition in the city of Peacetopia (case study).pdf ├── Course4_Convolutional Neural Networks │ ├── week1_Foundations of Convolutional Neural Networks │ │ ├── Convolution+model+-+Application+-+v1.ipynb │ │ ├── Convolution+model+-+Step+by+Step+-+v2.ipynb │ │ └── The basics of ConvNets.pdf │ ├── week2_Deep convolutional models case studies │ │ ├── Deep convolutional models.pdf │ │ └── Residual+Networks+-+v2.ipynb │ ├── week3_Object detection │ │ ├── Autonomous+driving+application+-+Car+detection+-+v1.ipynb │ │ └── Detection algorithms.pdf │ └── week4_Special applications Face recognition & Neural style transfer │ │ ├── Art+Generation+with+Neural+Style+Transfer+-+v2.ipynb │ │ ├── Face+Recognition+for+the+Happy+House+-+v3.ipynb │ │ └── Special applications Face recognition & Neural style transfer.pdf └── Course5_Sequence Models │ ├── week1_Recurrent Neural Networks │ ├── Building+a+Recurrent+Neural+Network+-+Step+by+Step+-+v1.ipynb │ ├── Dinosaurus+Island+--+Character+level+language+model+final+-+v1.ipynb │ ├── Improvise+a+Jazz+Solo+with+an+LSTM+Network+-+v1.ipynb │ └── Recurrent Neural Networks.pdf │ ├── week2_Natural Language Processing & Word Embeddings │ ├── Emojify+-+v1.ipynb │ ├── Natural Language Processing & Word Embeddings.pdf │ └── Operations+on+word+vectors+-+v1.ipynb │ └── week3_Sequence models & Attention mechanism │ ├── Machine Translation │ ├── Neural+machine+translation+with+attention+-+v1.ipynb │ ├── images │ │ ├── attn_mechanism.png │ │ ├── attn_model.png │ │ ├── date_attention.png │ │ ├── date_attention2.png │ │ ├── poorly_trained_model.png │ │ └── table.png │ └── nmt_utils.py │ ├── Sequence models & Attention mechanism.pdf │ └── Trigger+word+detection+-+v1.ipynb ├── Specialization_Functional_Programming_in_Scala └── Course1_Functional Programming Principles in Scala │ ├── forcomp │ ├── .idea │ │ ├── .name │ │ ├── libraries │ │ │ ├── SBT__junit_junit_4_10_jar.xml │ │ │ ├── SBT__org_hamcrest_hamcrest_core_1_1_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_parser_combinators_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_xml_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_library_2_11_7_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_reflect_2_11_7_jar.xml │ │ │ └── SBT__org_scalatest_scalatest_2_11_2_2_4_jar.xml │ │ ├── misc.xml │ │ ├── modules.xml │ │ ├── modules │ │ │ ├── root-build.iml │ │ │ └── root.iml │ │ ├── sbt.xml │ │ ├── scala_compiler.xml │ │ └── workspace.xml │ ├── assignment.sbt │ ├── build.sbt │ ├── project │ │ ├── CommonBuild.scala │ │ ├── Courses.scala │ │ ├── GradingFeedback.scala │ │ ├── ScalaTestRunner.scala │ │ ├── Settings.scala │ │ ├── StudentBuild.scala │ │ ├── StudentBuildLike.scala │ │ ├── StyleChecker.scala │ │ ├── build.properties │ │ ├── buildSettings.sbt │ │ └── plugins.sbt │ ├── scalastyle │ │ └── scalastyle_config.xml │ └── src │ │ ├── main │ │ ├── resources │ │ │ └── forcomp │ │ │ │ └── linuxwords.txt │ │ └── scala │ │ │ ├── common │ │ │ └── package.scala │ │ │ └── forcomp │ │ │ ├── Anagrams.scala │ │ │ └── package.scala │ │ └── test │ │ └── scala │ │ └── forcomp │ │ └── AnagramsSuite.scala │ ├── funsets │ ├── .idea │ │ ├── .name │ │ ├── libraries │ │ │ ├── SBT__junit_junit_4_10_jar.xml │ │ │ ├── SBT__org_hamcrest_hamcrest_core_1_1_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_parser_combinators_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_xml_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_library_2_11_7_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_reflect_2_11_7_jar.xml │ │ │ └── SBT__org_scalatest_scalatest_2_11_2_2_4_jar.xml │ │ ├── misc.xml │ │ ├── modules.xml │ │ ├── modules │ │ │ ├── root-build.iml │ │ │ └── root.iml │ │ ├── sbt.xml │ │ ├── scala_compiler.xml │ │ └── workspace.xml │ ├── assignment.sbt │ ├── build.sbt │ ├── project │ │ ├── CommonBuild.scala │ │ ├── Courses.scala │ │ ├── GradingFeedback.scala │ │ ├── ScalaTestRunner.scala │ │ ├── Settings.scala │ │ ├── StudentBuild.scala │ │ ├── StudentBuildLike.scala │ │ ├── StyleChecker.scala │ │ ├── build.properties │ │ ├── buildSettings.sbt │ │ └── plugins.sbt │ ├── scalastyle │ │ └── scalastyle_config.xml │ └── src │ │ ├── main │ │ └── scala │ │ │ ├── common │ │ │ └── package.scala │ │ │ └── funsets │ │ │ ├── FunSets.scala │ │ │ └── Main.scala │ │ └── test │ │ └── scala │ │ └── funsets │ │ └── FunSetSuite.scala │ ├── objsets │ ├── .idea │ │ ├── .name │ │ ├── libraries │ │ │ ├── SBT__junit_junit_4_10_jar.xml │ │ │ ├── SBT__org_hamcrest_hamcrest_core_1_1_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_parser_combinators_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_xml_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_library_2_11_7_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_reflect_2_11_7_jar.xml │ │ │ └── SBT__org_scalatest_scalatest_2_11_2_2_4_jar.xml │ │ ├── misc.xml │ │ ├── modules.xml │ │ ├── modules │ │ │ ├── root-build.iml │ │ │ └── root.iml │ │ ├── sbt.xml │ │ ├── scala_compiler.xml │ │ └── workspace.xml │ ├── assignment.sbt │ ├── build.sbt │ ├── project │ │ ├── CommonBuild.scala │ │ ├── Courses.scala │ │ ├── GradingFeedback.scala │ │ ├── ScalaTestRunner.scala │ │ ├── Settings.scala │ │ ├── StudentBuild.scala │ │ ├── StudentBuildLike.scala │ │ ├── StyleChecker.scala │ │ ├── build.properties │ │ ├── buildSettings.sbt │ │ └── plugins.sbt │ ├── scalastyle │ │ └── scalastyle_config.xml │ └── src │ │ ├── main │ │ └── scala │ │ │ ├── common │ │ │ └── package.scala │ │ │ └── objsets │ │ │ ├── TweetData.scala │ │ │ ├── TweetReader.scala │ │ │ └── TweetSet.scala │ │ └── test │ │ └── scala │ │ └── objsets │ │ └── TweetSetSuite.scala │ ├── patmat │ ├── .idea │ │ ├── .name │ │ ├── libraries │ │ │ ├── SBT__junit_junit_4_10_jar.xml │ │ │ ├── SBT__org_hamcrest_hamcrest_core_1_1_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_parser_combinators_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_modules_scala_xml_2_11_1_0_4_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_library_2_11_7_jar.xml │ │ │ ├── SBT__org_scala_lang_scala_reflect_2_11_7_jar.xml │ │ │ └── SBT__org_scalatest_scalatest_2_11_2_2_4_jar.xml │ │ ├── misc.xml │ │ ├── modules.xml │ │ ├── modules │ │ │ ├── root-build.iml │ │ │ └── root.iml │ │ ├── sbt.xml │ │ ├── scala_compiler.xml │ │ └── workspace.xml │ ├── assignment.sbt │ ├── build.sbt │ ├── project │ │ ├── CommonBuild.scala │ │ ├── Courses.scala │ │ ├── GradingFeedback.scala │ │ ├── ScalaTestRunner.scala │ │ ├── Settings.scala │ │ ├── StudentBuild.scala │ │ ├── StudentBuildLike.scala │ │ ├── StyleChecker.scala │ │ ├── build.properties │ │ ├── buildSettings.sbt │ │ └── plugins.sbt │ ├── scalastyle │ │ └── scalastyle_config.xml │ └── src │ │ ├── main │ │ └── scala │ │ │ ├── common │ │ │ └── package.scala │ │ │ └── patmat │ │ │ └── Huffman.scala │ │ └── test │ │ └── scala │ │ └── patmat │ │ └── HuffmanSuite.scala │ └── recfun │ ├── .idea │ ├── .name │ ├── libraries │ │ ├── SBT__junit_junit_4_10_jar.xml │ │ ├── SBT__org_hamcrest_hamcrest_core_1_1_jar.xml │ │ ├── SBT__org_scala_lang_modules_scala_parser_combinators_2_11_1_0_4_jar.xml │ │ ├── SBT__org_scala_lang_modules_scala_xml_2_11_1_0_4_jar.xml │ │ ├── SBT__org_scala_lang_scala_library_2_11_7_jar.xml │ │ ├── SBT__org_scala_lang_scala_reflect_2_11_7_jar.xml │ │ └── SBT__org_scalatest_scalatest_2_11_2_2_4_jar.xml │ ├── misc.xml │ ├── modules.xml │ ├── modules │ │ ├── root-build.iml │ │ └── root.iml │ ├── sbt.xml │ ├── scala_compiler.xml │ └── workspace.xml │ ├── assignment.sbt │ ├── build.sbt │ ├── project │ ├── CommonBuild.scala │ ├── Courses.scala │ ├── GradingFeedback.scala │ ├── ScalaTestRunner.scala │ ├── Settings.scala │ ├── StudentBuild.scala │ ├── StudentBuildLike.scala │ ├── StyleChecker.scala │ ├── build.properties │ ├── buildSettings.sbt │ └── plugins.sbt │ ├── scalastyle │ └── scalastyle_config.xml │ └── src │ ├── main │ └── scala │ │ ├── common │ │ └── package.scala │ │ └── recfun │ │ └── Main.scala │ └── test │ └── scala │ └── recfun │ ├── BalanceSuite.scala │ ├── CountChangeSuite.scala │ └── PascalSuite.scala ├── Specialization_Fundamentals_of_Computing_Rice_University └── Course3_Principles_of_Computing_1 │ ├── 2048(Full).py │ ├── 2048(Merge).py │ ├── Tic-Tac-Toe (Monte Carlo).py │ └── week4.pdf ├── Specialization_Machine_Learning_University_of_Washington ├── Course1_ML_Foundations │ ├── week2_Regression-Predicting House Prices │ │ ├── Predicting House Prices_gl.ipynb │ │ ├── Predicting House Prices_skl.ipynb │ │ ├── Predicting house prices.pdf │ │ └── Regression.pdf │ ├── week3_Classification-Analyzing Sentiment │ │ ├── Analyzing Product Sentiment_gl.ipynb │ │ ├── Analyzing Product Sentiment_skl.ipynb │ │ ├── Analyzing product sentiment.pdf │ │ └── Classification.pdf │ ├── week4_Clustering and Similarity-Retrieving Documents │ │ ├── Clustering and Similarity.pdf │ │ ├── Document Retrieval_gl.ipynb │ │ ├── Document Retrieval_skl.ipynb │ │ └── Retrieving Wikipedia articles.pdf │ ├── week5_Recommending Products │ │ ├── Recommender Systems.pdf │ │ ├── Recommending songs.pdf │ │ └── Song Recommender_gl.ipynb │ └── week6_Deep Learning-Searching for Images │ │ ├── Deep Features for Image Classification_gl.ipynb │ │ ├── Deep Features for Image Classification_skl.ipynb │ │ ├── Deep Features for Image Retrieval_gl.ipynb │ │ ├── Deep Features for Image Retrieval_skl.ipynb │ │ ├── Deep Learning.pdf │ │ └── Deep features for image retrieval.pdf ├── Course2_ML_Regression │ ├── README.md │ ├── week1_Simple Linear Regression │ │ ├── Fitting a simple linear regression model on housing data.pdf │ │ ├── PhillyCrime.ipynb │ │ ├── Simple Linear Regression.pdf │ │ ├── week-1-simple-regression-assignment_gl.ipynb │ │ └── week-1-simple-regression-assignment_skl.ipynb │ ├── week2_Multiple Regression │ │ ├── Exploring different multiple regression models for house price prediction.pdf │ │ ├── Implementing gradient descent for multiple regression.pdf │ │ ├── Multiple Regression.pdf │ │ ├── numpy-tutorial.ipynb │ │ ├── week-2-multiple-regression-assignment-1_gl.ipynb │ │ ├── week-2-multiple-regression-assignment-1_skl.ipynb │ │ ├── week-2-multiple-regression-assignment-2_gl.ipynb │ │ └── week-2-multiple-regression-assignment-2_skl.ipynb │ ├── week3_Assessing Performance │ │ ├── Assessing Performance.pdf │ │ ├── Exploring the bias-variance tradeoff.pdf │ │ ├── week-3-polynomial-regression-assignment_gl.ipynb │ │ └── week-3-polynomial-regression-assignment_skl.ipynb │ ├── week4_Ridge Regression │ │ ├── Implementing ridge regression via gradient descent.pdf │ │ ├── Observing effects of L2 penalty in polynomial regression.pdf │ │ ├── Ridge Regression.pdf │ │ ├── week-4-ridge-regression-assignment-1_gl.ipynb │ │ ├── week-4-ridge-regression-assignment-1_skl.ipynb │ │ ├── week-4-ridge-regression-assignment-2_gl.ipynb │ │ └── week-4-ridge-regression-assignment-2_skl.ipynb │ ├── week5_Feature Selection & Lasso │ │ ├── Feature Selection and Lasso.pdf │ │ ├── Implementing LASSO using coordinate descent.pdf │ │ ├── Overfitting_Demo_Ridge_Lasso.ipynb │ │ ├── Using LASSO to select features.pdf │ │ ├── week-5-lasso-assignment-1-skl.ipynb │ │ ├── week-5-lasso-assignment-1_gl.ipynb │ │ ├── week-5-lasso-assignment-2_gl.ipynb │ │ └── week-5-lasso-assignment-2_skl.ipynb │ └── week6_Nearest Neighbors & Kernel Regression │ │ ├── Nearest Neighbors & Kernel Regression.pdf │ │ ├── Predicting house prices using k-nearest neighbors regression.pdf │ │ ├── week-6-local-regression-assignment_gl.ipynb │ │ └── week-6-local-regression-assignment_skl.ipynb ├── Course3_ML_Classification │ ├── week1_Linear Classifiers & Logistic Regression │ │ ├── Linear Classifiers & Logistic Regression.pdf │ │ ├── Predicting sentiment from product reviews.pdf │ │ ├── module-2-linear-classifier-assignment_gl.ipynb │ │ └── module-2-linear-classifier-assignment_skl.ipynb │ ├── week2_Learning Linear Classifiers │ │ ├── Implementing logistic regression from scratch.pdf │ │ ├── Learning Linear Classifiers.pdf │ │ ├── Logistic Regression with L2 regularization.pdf │ │ ├── Overfitting & Regularization in Logistic Regression.pdf │ │ ├── module-3-linear-classifier-learning-assignment_gl.ipynb │ │ ├── module-3-linear-classifier-learning-assignment_skl.ipynb │ │ ├── module-4-linear-classifier-regularization-assignment_gl.ipynb │ │ └── module-4-linear-classifier-regularization-assignment_skl.ipynb │ ├── week3_Decision Trees │ │ ├── Decision Trees.pdf │ │ ├── Identifying safe loans with decision trees.pdf │ │ ├── Implementing binary decision trees.pdf │ │ ├── module-5-decision-tree-assignment-1_gl.ipynb │ │ ├── module-5-decision-tree-assignment-1_skl.ipynb │ │ ├── module-5-decision-tree-assignment-2_gl.ipynb │ │ └── module-5-decision-tree-assignment-2_skl.ipynb │ ├── week4_Preventing Overfitting in Decision Trees & Handling Missing Data │ │ ├── Decision Trees in Practice.pdf │ │ ├── Handling Missing Data.pdf │ │ ├── Preventing Overfitting in Decision Trees.pdf │ │ ├── module-6-decision-tree-practical-assignment_gl.ipynb │ │ └── module-6-decision-tree-practical-assignment_skl.ipynb │ ├── week5_Boosting │ │ ├── Boosting a decision stump.pdf │ │ ├── Boosting.pdf │ │ ├── Exploring Ensemble Methods.pdf │ │ ├── module-8-boosting-assignment-1_gl.ipynb │ │ ├── module-8-boosting-assignment-1_skl.ipynb │ │ └── module-8-boosting-assignment-2_gl.ipynb │ ├── week6_Precision-Recall │ │ ├── Exploring precision and recall.pdf │ │ ├── Precision-Recall.pdf │ │ └── module-9-precision-recall-assignment_gl.ipynb │ └── week7_Scaling to Huge Datasets & Online Learning │ │ ├── Scaling to Huge Datasets & Online Learning.pdf │ │ ├── Training Logistic Regression via Stochastic Gradient Ascent.pdf │ │ └── module-10-online-learning-assignment-blank.ipynb └── Course4_ML_Clustering_and_Retrieval │ ├── week2_Nearest Neighbor Search │ ├── 0_nearest-neighbors-features-and-metrics_gl.ipynb │ ├── 0_nearest-neighbors-features-and-metrics_skl.ipynb │ ├── 1_nearest-neighbors-lsh-implementation_gl.ipynb │ ├── 1_nearest-neighbors-lsh-implementation_skl.ipynb │ ├── Choosing features and metrics for nearest neighbor search.pdf │ ├── Implementing Locality Sensitive Hashing from scratch.pdf │ ├── KD-trees.pdf │ ├── Locality Sensitive Hashing.pdf │ └── Representations and metrics.pdf │ ├── week3_Clustering with k-means │ ├── 2_kmeans-with-text-data_gl.ipynb │ ├── 2_kmeans-with-text-data_skl.ipynb │ ├── Clustering text data with K-means.pdf │ ├── MapReduce for k-means.pdf │ └── k-means.pdf │ ├── week4_Mixture Models │ ├── 3_em-for-gmm_gl.ipynb │ ├── 3_em-for-gmm_skl.ipynb │ ├── 4_em-with-text-data_gl.ipynb │ ├── 4_em-with-text-data_skl.ipynb │ ├── Clustering text data with Gaussian mixtures.pdf │ ├── EM for Gaussian mixtures.pdf │ ├── Implementing EM for Gaussian mixtures.pdf │ └── em_utilities.py │ ├── week5_Mixed Membership Modeling via Latent Dirichlet Allocation │ ├── 5_lda_gl.ipynb │ ├── Latent Dirichlet Allocation.pdf │ ├── Learning LDA model via Gibbs sampling.pdf │ └── Modeling text topics with Latent Dirichlet Allocation.pdf │ └── week6_Hierarchical Clustering & Closing Remarks │ ├── 6_hierarchical_clustering_gl.ipynb │ ├── 6_hierarchical_clustering_skl.ipynb │ └── Modeling text data with a hierarchy of clusters.pdf ├── Specialization_Probabilistic_Graphical_Models_Stanford_University ├── Course1_Probabilistic_Graphical_Models_1_Representation │ ├── HW1_Simple BN Knowledge Engineering │ │ ├── AssignmentToIndex.m │ │ ├── ComputeJointDistribution.m │ │ ├── ComputeMarginal.m │ │ ├── ConvertNetwork.m │ │ ├── Credit_net.net │ │ ├── FactorMarginalization.m │ │ ├── FactorProduct.m │ │ ├── FactorTutorial.m │ │ ├── GetValueOfAssignment.m │ │ ├── IndexToAssignment.m │ │ ├── ObserveEvidence.m │ │ ├── PA-Simple-BN-Knowledge-Engineering.pdf │ │ ├── SetValueOfAssignment.m │ │ ├── lib │ │ │ ├── jsonlab │ │ │ │ ├── AUTHORS.txt │ │ │ │ ├── ChangeLog.txt │ │ │ │ ├── LICENSE_BSD.txt │ │ │ │ ├── README.txt │ │ │ │ ├── jsonopt.m │ │ │ │ ├── loadjson.m │ │ │ │ ├── loadubjson.m │ │ │ │ ├── mergestruct.m │ │ │ │ ├── savejson.m │ │ │ │ ├── saveubjson.m │ │ │ │ └── varargin2struct.m │ │ │ ├── makeValidFieldName.m │ │ │ └── submitWithConfiguration.m │ │ ├── submit.m │ │ ├── submit_input.mat │ │ └── token.mat │ ├── HW3_Markov Networks for OCR │ │ ├── AssignmentToIndex.m │ │ ├── BuildOCRNetwork.m │ │ ├── ChooseTopSimilarityFactors.m │ │ ├── ComputeAllSimilarityFactors.m │ │ ├── ComputeEqualPairwiseFactors.m │ │ ├── ComputeImageFactor.m │ │ ├── ComputePairwiseFactors.m │ │ ├── ComputeSimilarityFactor.m │ │ ├── ComputeSingletonFactors.m │ │ ├── ComputeTripletFactors.m │ │ ├── ComputeWordPredictions.m │ │ ├── GetValueOfAssignment.m │ │ ├── ImageSimilarity.m │ │ ├── IndexToAssignment.m │ │ ├── PA-Markov-Networks-for-OCR.pdf │ │ ├── PA3Data.mat │ │ ├── PA3Models.mat │ │ ├── PA3SampleCases.mat │ │ ├── PA3TestCases.mat │ │ ├── RunInference.m │ │ ├── ScoreModel.m │ │ ├── ScorePredictions.m │ │ ├── SerializeFactorsFg.m │ │ ├── SerializeFactorsFgGrading.m │ │ ├── SetValueOfAssignment.m │ │ ├── VisualizeWord.m │ │ ├── inference │ │ │ ├── doinference-linux │ │ │ ├── doinference-mac │ │ │ ├── doinference.exe │ │ │ ├── inference-src.zip │ │ │ └── sofiakp@corn.stanford.edu │ │ ├── lib │ │ │ ├── jsonlab │ │ │ │ ├── AUTHORS.txt │ │ │ │ ├── ChangeLog.txt │ │ │ │ ├── LICENSE_BSD.txt │ │ │ │ ├── README.txt │ │ │ │ ├── jsonopt.m │ │ │ │ ├── loadjson.m │ │ │ │ ├── loadubjson.m │ │ │ │ ├── mergestruct.m │ │ │ │ ├── savejson.m │ │ │ │ ├── saveubjson.m │ │ │ │ └── varargin2struct.m │ │ │ ├── makeValidFieldName.m │ │ │ └── submitWithConfiguration.m │ │ ├── submit.m │ │ └── token.mat │ ├── week1_Bayesian Network Fundamentals.pdf │ ├── week1_Bayesian Network Independencies.pdf │ ├── week2_Structured CPDs.pdf │ ├── week2_Template Models.pdf │ ├── week3_Independencies Revisited.pdf │ ├── week3_Markov Networks.pdf │ ├── week4_Decision Theory.pdf │ └── week5_Representation Final Exam.pdf ├── Course2_Probabilistic_Graphical_Models_2_Inference │ ├── README.md │ ├── week1_Variable Elimination.pdf │ ├── week2_Clique Tree Algorithm.pdf │ ├── week2_Message Passing in Cluster Graphs.pdf │ ├── week3_MAP Message Passing.pdf │ ├── week4_Inference in Temporal Models.pdf │ ├── week4_Sampling Methods.pdf │ └── week5_Inference Final Exam.pdf └── Course3_Probabilistic_Graphical_Models_3_Learning │ ├── README.md │ ├── week1_Bayesian Priors for BNs.pdf │ ├── week1_Learning in Parametric Models.pdf │ ├── week2_Parameter Estimation in MNs.pdf │ ├── week3_Structure Scores.pdf │ ├── week3_Tree Learning and Hill Climbing.pdf │ ├── week4_Expectation Maximization.pdf │ ├── week4_Learning with Incomplete Data.pdf │ └── week5_Learning Final Exam.pdf ├── Specialization_Program_Design_Algorithm_Peking_University ├── Course1_jisuanji-biancheng │ ├── week10 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ ├── source5.cpp │ │ ├── source6.cpp │ │ ├── source7.cpp │ │ └── source8.cpp │ ├── week12 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ └── source5.cpp │ ├── week4 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ └── source3.cpp │ ├── week5 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ └── source5.cpp │ ├── week6 │ │ ├── source0.cpp │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ └── source5.cpp │ ├── week7 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ └── source4.cpp │ └── week8 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ └── source5.cpp ├── Course2_c-chengxu-sheji │ ├── README.md │ ├── week2_从C走进C++ │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ ├── source5_copy.cpp │ │ └── source6.cpp │ ├── week3_函数的递归 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ └── source4.cpp │ ├── week5_指针与二维数组 │ │ ├── source1.cpp │ │ ├── source2_copy.cpp │ │ └── source3_copy.cpp │ ├── week6_指针（三） │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ └── source3.cpp │ └── week9_final │ │ ├── source1_copy.cpp │ │ ├── source2_copy.cpp │ │ ├── source3_copy.cpp │ │ ├── source4_copy.cpp │ │ └── source5_copy.cpp ├── Course3_cpp-chengxu-sheji │ ├── READEME.md │ ├── week10_final │ │ ├── source1.cpp │ │ ├── source10.cpp │ │ ├── source11.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ ├── source5.cpp │ │ ├── source6.cpp │ │ ├── source7.cpp │ │ ├── source8.cpp │ │ └── source9.cpp │ ├── week2_从C走进C++ │ │ └── source1.cpp │ ├── week3_类和对象 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ └── source4.cpp │ ├── week4_运算符重载 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ └── source4_copy.cpp │ ├── week5_继承与派生 │ │ ├── source1.cpp │ │ └── source2.cpp │ ├── week6_多态与虚函数 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ └── source4_copy.cpp │ ├── week7_文件操作与模板 │ │ ├── source1.cpp │ │ ├── source2.cpp │ │ ├── source3.cpp │ │ └── source4.cpp │ ├── week8_STL1 │ │ ├── source1.cpp │ │ └── source2.cpp │ └── week9_STL2 │ │ ├── source1.cpp │ │ ├── source2_copy.cpp │ │ ├── source3.cpp │ │ ├── source4.cpp │ │ ├── source5.cpp │ │ └── source6_copy.cpp ├── Course4_suanfa-jichu │ ├── week2_枚举 │ │ └── source_1.cpp │ ├── week3_递归 │ │ ├── source1.cpp │ │ └── source2.cpp │ ├── week4_动规1 │ │ ├── source1.cpp │ │ └── source2.cpp │ ├── week5_动规2 │ │ └── source2.cpp │ └── week6_深搜1 │ │ ├── source_1.cpp │ │ └── source_2.cpp └── Course5_shuju-jiegou-suanfa │ ├── README.md │ ├── week1_概论 │ └── 概论.pdf │ ├── week2_线性表 │ ├── source1.cpp │ ├── source2.cpp │ ├── source3.cpp │ └── 线性表.pdf │ ├── week3_栈与队列 │ ├── source1.cpp │ └── source2_copy.cpp │ ├── week4_字符串 │ ├── source1.cpp │ └── source2.cpp │ ├── week5_二叉树基础 │ ├── source1.cpp │ └── source2.cpp │ ├── week6_二叉树应用 │ ├── source1.cpp │ └── source2.cpp │ ├── week7_树 │ └── source1.cpp │ └── week8_图 │ ├── source1.cpp │ └── source2.cpp ├── Specialization_Recommender_System_University_of_Minnesota ├── Course1_Introduction_to_Recommender_Systems_Non_Personalized_and_Content_Based │ ├── A1_Non-Personalized and Stereotype-Based Recommenders │ │ ├── #1 Top Movies by Mean Rating.pdf │ │ ├── #2 Top Movies by Count.pdf │ │ ├── #3 Top Movies by Percent Liking.pdf │ │ ├── #4 Association with Toy Story.pdf │ │ ├── #5 Correlation with Toy Story.pdf │ │ ├── #6 MF Differences in Average Rating.pdf │ │ ├── #7 Male-Female differences in Liking.pdf │ │ ├── Assignment #1.ipynb │ │ └── Closing Quiz Introducing Recommender Systems.pdf │ └── A2_Content-Based Filtering │ │ ├── Assignment #2 Answer Form.pdf │ │ ├── Assignment #2.ipynb │ │ ├── Assignment 2.xls │ │ └── Content-Based Filtering.pdf ├── Course2_Nearest_Neighbor_Collaborative_Filtering │ ├── Assignment 5.xls │ ├── Item Based Assignment Part II.pdf │ ├── Item Based Assignment Part III.pdf │ ├── Item Based Assignment Part IV.pdf │ ├── Item Based Assignment Part l.pdf │ ├── Item Based Assignment.ipynb │ ├── Item-Based and Advanced Collaborative Filtering Topics Quiz.pdf │ ├── UUCF Assignment Spreadsheet.xls │ ├── User-User CF Answer Sheet.ipynb │ ├── User-User CF Answer Sheet.pdf │ └── User-User Collaborative Filtering Quiz.pdf ├── Course3_Recommender_Systems_Evaluation_and_Metrics │ ├── Basic Prediction and Recommendation Metrics Assignment.pdf │ ├── Evaluation Design Cases.pdf │ ├── Metric-SpreadSheet-Assignment.ipynb │ ├── Metric-SpreadSheet-Assignment.xlsx │ ├── Offline Evaluation and Metrics Quiz.pdf │ └── Online Evaluation Quiz.pdf └── Course4_Matrix_Factorization_and_Advanced_Techniques │ ├── Assignment_6.xls │ ├── Hybrid and Advanced Techniques Quiz.pdf │ ├── Matrix Factorization Assignment Part l.pdf │ ├── Matrix Factorization Assignment Part ll.pdf │ ├── Matrix Factorization Assignment Part lll.pdf │ ├── Matrix Factorization Assignment.ipynb │ └── Matrix Factorization Quiz.pdf ├── Specialization_Statistics_with_R_Duke_University ├── Course1_Introduction_to_Probability_and_Data │ ├── week1_Introduction to Data.Rmd │ ├── week2_Exploratory Data Analysis and Introduction to Inference.Rmd │ └── week3_Introduction to Probability.Rmd └── Course2_Inferential_Statistics │ ├── week1_sampling_distributions_Coursera.Rmd │ └── week2_confidence_intervals_Coursera.rmd ├── The_Unix_Workbench_Johns_Hopkins_University ├── Bash Programming.pdf ├── Command Line Basics.pdf ├── Git & GitHub.pdf ├── Nephology.pdf ├── Working with Unix.pdf └── guessinggame │ ├── README.md │ ├── guessinggame.sh │ └── makefile └── 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations ├── 作業一.pdf └── 作業二.pdf /.gitattributes: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shenweichen/Coursera/HEAD/.gitattributes -------------------------------------------------------------------------------- /.gitignore: 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