Data and Knowledge Modeling and Analysis
Week 10 - Live Session - ASG2, TEST 2, PCA FEATURES, DEEP LEARNING SCHEDULE - ECE657A - Spring 2021
59:11
Data and Knowledge Modeling and Analysis
Week 9 - Live Session - ECE657A - Spring 2021
32:55
Data and Knowledge Modeling and Analysis
Week 8 - Live Session - Post-Test 1, Clustering, Train vs.Test - ECE657A - Spring 2021
20:00
Data and Knowledge Modeling and Analysis
Week 7 - Live Session - Review and Test 1 - 657A - Spring 2021
57:08
Data and Knowledge Modeling and Analysis
Maximum a Posteriori (MAP) Estimation
21:19
Data and Knowledge Modeling and Analysis
S21 - Week1Review - 3 - Questions
26:06
Data and Knowledge Modeling and Analysis
Week1Review - 1 - S21 - Poll - Admin
25:13
Data and Knowledge Modeling and Analysis
S21 - Week1Review - 2 - Questions
23:27
Data and Knowledge Modeling and Analysis
Visualizing CNNs
22:35
Data and Knowledge Modeling and Analysis
Autoencoders
28:29
Data and Knowledge Modeling and Analysis
Denoising Autoencoders
21:19
Data and Knowledge Modeling and Analysis
Deep Learning by Gradient Descent
25:18
Data and Knowledge Modeling and Analysis
Deep Learning Fundamentals
26:50
Data and Knowledge Modeling and Analysis
Lect 11B - Admin Intro
6:49
Data and Knowledge Modeling and Analysis
Effective Deep Learning - Data Augmentation and Vanishing Gradients
17:38
Data and Knowledge Modeling and Analysis
Effective Deep Learning - Regularization
22:54
Data and Knowledge Modeling and Analysis
Deep Learning - Fundamentals III (optional)
20:27
Data and Knowledge Modeling and Analysis
Convolutional Neural Networks II
33:57
Data and Knowledge Modeling and Analysis
CNNs - Making Deeper Networks
25:52
Data and Knowledge Modeling and Analysis
Lect 12B - 1 - Admin and Announcements
5:32
Data and Knowledge Modeling and Analysis
Practical Methodology : Hyper-parameter Tuning
22:00
Data and Knowledge Modeling and Analysis
Adding Time : Recurrent Neural Networks
18:23
Data and Knowledge Modeling and Analysis
Inception - Resnet - Densenet
17:46
Data and Knowledge Modeling and Analysis
Convolutional Neural Networks I
17:38
Data and Knowledge Modeling and Analysis
DeepLearning - Fundamentals II (optional)
17:56
Data and Knowledge Modeling and Analysis
Introduction to Neural Networks Concepts and History
38:25
Data and Knowledge Modeling and Analysis
Anomaly Detection Using One Class SVM (optional)
16:48
Data and Knowledge Modeling and Analysis
Anomaly Detection Isolation Forests and Mondrian Forests
43:15
Data and Knowledge Modeling and Analysis
Lect 9A-1 - Admin and Asg 3
31:24
Data and Knowledge Modeling and Analysis
Anomaly Detection Using Density Estimation
12:08
Data and Knowledge Modeling and Analysis
Anomaly Detection Definitions and Classic Approaches
25:28
Data and Knowledge Modeling and Analysis
Clustering - Density Based - DBScan
17:54
Data and Knowledge Modeling and Analysis
Unsupervised Learning - Clustering Definitions and Hierarchal Algorithms
39:15
Data and Knowledge Modeling and Analysis
Lect 8B-1 Course Admin
15:25
Data and Knowledge Modeling and Analysis
Clustering - Partition Based - kMeans
20:39
Data and Knowledge Modeling and Analysis
Support Vector Machines
1:14:33
Data and Knowledge Modeling and Analysis
Lect 7B - Document Representation - TF-IDF
19:29
Data and Knowledge Modeling and Analysis
Lect 7B - Document Representation - Examples and GloVe
19:28
Data and Knowledge Modeling and Analysis
Lect 7B - Document Representation - word2vec
30:02
Data and Knowledge Modeling and Analysis
Lect 7A - Document Representation
23:55
Data and Knowledge Modeling and Analysis
Streaming Ensembles Continued - Hoeffding Trees and Mondrian Forests (optional)
40:37
Data and Knowledge Modeling and Analysis
Week 7 - Announcements, Questions about Assignment 2
21:39
Data and Knowledge Modeling and Analysis
What is Data? Data Preprocessing, Cleaning and Normalization
41:32
Data and Knowledge Modeling and Analysis
Ensemble Methods
48:13
Data and Knowledge Modeling and Analysis
Extremely Randomized Trees, Gradient Boosting (optional after 16:27 - Hoeffding Trees)
28:29
Data and Knowledge Modeling and Analysis
Lect 4B - Admin and Questions
9:27
Data and Knowledge Modeling and Analysis
Clustering Evaluation Measures
35:20
Data and Knowledge Modeling and Analysis
Overview - Feature Selection, Extraction and Representation Learning
48:23
Data and Knowledge Modeling and Analysis
Lect 4A - Admin and Announcements
6:11
Data and Knowledge Modeling and Analysis
Decision Trees Pruning and Implementations
22:07
Data and Knowledge Modeling and Analysis
Decision Tree Costs Evaluation
28:02
Data and Knowledge Modeling and Analysis
Decision Trees - Definition
24:32
Data and Knowledge Modeling and Analysis
Variational Autoencoder (VAE) (optional)
40:07
Data and Knowledge Modeling and Analysis
Independent Component Analysis (ICA) Using Singular Value Decomposition (optional)
35:32
Data and Knowledge Modeling and Analysis
t-Stochastic Neighbor Embedding (t-SNE)
36:59
Data and Knowledge Modeling and Analysis
Locally Linear Embedding (LLE) (optional)
38:27
Data and Knowledge Modeling and Analysis
Multidimensional Scaling (MDS) and Isomap (optional)
43:13
Data and Knowledge Modeling and Analysis
Fisher Discriminant Analysis (FDA) and Linear Discriminant Analysis (LDA)
53:56
Data and Knowledge Modeling and Analysis
Principal Component Analysis (PCA)
54:16
Data and Knowledge Modeling and Analysis
Logistic Regression with MLE and MAP
18:23
Data and Knowledge Modeling and Analysis
Naive Bayes Classifier and Expectation Maximization
28:07
Data and Knowledge Modeling and Analysis
Parameter Estimation - Bias vs. Variance - Unbiased Estimators - Cross-validation
36:35
Data and Knowledge Modeling and Analysis
Maximum Likelihood Estimation
17:45
Data and Knowledge Modeling and Analysis
Lect 3A - Course Admin
11:02
Data and Knowledge Modeling and Analysis
Performance Evaluation - ROC and PR Curves
42:14
Data and Knowledge Modeling and Analysis
Performance Evaluation - The Confusion Matrix
25:32
Data and Knowledge Modeling and Analysis
Data and Knowledge Modeling and Analysis Live Stream
2:08
Data and Knowledge Modeling and Analysis
Classification - Non Parametric Methods, kNN, Parzen Windows
39:28
Data and Knowledge Modeling and Analysis
Classification - Introduction
30:06
Data and Knowledge Modeling and Analysis
Week 2A Course Admin
7:48
Data and Knowledge Modeling and Analysis
Data and Knowledge Modeling and Analysis Live Stream
Data and Knowledge Modeling and Analysis
Week 2S Part 1 - ProbStatsReview -Conditional Prob and Bayes Theorem
47:30
Data and Knowledge Modeling and Analysis
Week 2S 1.2 - Comparing Distributions - Random Variables
18:24
Data and Knowledge Modeling and Analysis
Week 2S 1.3 Hypothesis Testing
30:15
Data and Knowledge Modeling and Analysis
Measuring Similarity of Data - Distance Metrics
27:35
Data and Knowledge Modeling and Analysis
Measuring Similarity of Data
18:17
Data and Knowledge Modeling and Analysis
Training and Validation Methodology - Test, Train, and Validate
24:51
Data and Knowledge Modeling and Analysis
Training and Validation Methodology - Ablation Studies
17:16