Year: 2021

In this homework, I am working on Expectation Maximization (EM) Algorithm and Auxiliary function. Handwritten answers and codes are attached in the report.

This homework is mainly about gradient based learning, either first-order gradient or Newton’s Method. I am asked to derive the converging speed and error bounds. After that, two real applications — stock market prediction and handwritten digit classification problems — are given for us to practice. The experiment results and codes are written in report.

In this homework, I need to work on maximum likelihood estimation in different circumstances. For the third part of the homework, I am asked to work on calculating the maximum likelihood estimation of unigrams and bigrams for a real dataset. Codes are attached in the report.

For third homework, I am still using the basic probabilistic knowledge to do inference in either a chain or polytrees. Codes are included in report.

In the second homework, I am first required to work on probabilistic inference, probabilistic reasoning, sigmoid function. Later, I need to simplify a given probabilistic formula based on conditional independence between variables, including three independent rules and Markov Blanket rule.

First homework in this course. It is about the basic probability knowledge, such as conditional probability, Bayesian rule, entropy, Kullback-Leibler (KL) distance, Mutual Information. Codes are attached in report.

For the written part, one is asked to work on nearest neighbor algorithm, and Principle Component Analysis (PCA). Later on, naive recognition, k-Nearest Neighbor (k-NN) recognition, Eigenfaces recognition, and Fisherfaces recognition are required separately. It is interesting that for eigenfaces recognition, dropping top four eigenvfaces actually induce better learning results. This might be caused by

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In this homework, we work on several topics: image warping and merging, finding optical flows (dense or sparse or iterative from coarse to fine), background subtraction, motion segmentation, and finding hough lines. For the first topic, one is basically going to repeat what we have done in last homework — corner detection + mapping. For

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Oct 2016 – Nov 2016 For the written part of this homework, I need to do some calculations based on Epipolar Geometry. On the other hand, for the coding part, there are mainly three tasks: draw bound boxes, edge detection, and sparse stereo matching. In the first task, I need to figure out the proper

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In this homework, I need to deal with perspective projection calculation, derive image manipulation matrices (translation or translation + rotation), and image rendering with gradients of image intensity. Codes are also attached inside the report.