The C.S. PhD program has a course waiver process, and given that so many of the students come from similar institutions, there should really be a pre-approved list of courses to transfer over for every area, like there is for "Software Systems". But no, I had to fill out the course waiver form in its entirety, and didn't want to risk annoying the approver by leaving fields blank because I assumed they were familiar with the course. But the least I can do is dump the info here for the two courses I did apply for a waiver for (CS 173 and CS 161, using 6.047 and 6.046 from MIT respectively)

## General Info

### Stanford course to be waived

: CS 173### Advisor name

: Gill Bejerano## Non-Stanford Course Info

### University Name

: Massachusetts Institute of Technology### Instructor

: Manolis Kellis### Year

: 2012### Course website

: http://stellar.mit.edu/S/course/6/fa12/6.047/index.html### Link to course assignments

: http://stellar.mit.edu/S/course/6/fa12/6.047/materials.html### Description:

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks.

### Syllabus:

Dynamic Programming, Global and local alignment

Database search, Rapid string matching, BLAST, BLOSUM

Multiple, Progressive, Phylogenetic, Whole-genome alignment

Whole-Genome Comparative genomics: Evolutionary signatures, Duplication

Genome Assembly: Consensus-alignment-overlap, Graph-based assembly

Hidden Markov Models Part 1: Evaluation / Parsing, Viterbi/Forward algorithm

Hidden Markov Models Part 2: PosteriorDecoding / Learning Baum Welch

Structural RNAs: Fold prediction, genome-wide annotation

Transcript structure analysis, Differential Expression, Significance Testing

Small and Large regulatory RNAs: lincRNA, miRNA, piRNA

Expression analysis: Clustering and Classification, K-means, Naïve-Bayes

Clustering: affinity propagation; Classification: Random Forests

Epigenomics: ChIP-Seq, Burrows-Wheeler alignment, Chromatin States

Regulatory Motifs: Discovery, Representation, PBMs, Gibbs Sampling, EM

Expression deconvolution functional data of mixed samples

Network Inference: Physical and functional networks, information integration

The ENCODE Project: Systematic experimentation and integrative genomics

Dimentionality reduction, PCA, Self-Organizing Maps, SVMs

Disease association mapping, GWAS, organismal phenotypes

Quantitative trait mapping, eQTLs, molecular trait variation

Linkage Disequilibrium, Haplotype phasing, variant imputat

Molecular Evolution, Tree Building, Phylogenetic inference

Gene/species trees, reconciliation, recombination graphs

Mutation rate estimation, coalescent, models of evolution

Missing Heritability, Complex Traits, Interpret GWAS, Rank-based enrichment

Recent human evolution: Human history, human selection

Personal Genomics, Disease Epigenomics: Systems approaches to disease

Three-dimentional chromatin interactions: 3C, 5C, HiC, ChIA-Pet

Pharmacogenomics: Network-based systems biology of drug response

Synthetic Biology: Reading and writing genomes and cellular circuits

+ Final project

Database search, Rapid string matching, BLAST, BLOSUM

Multiple, Progressive, Phylogenetic, Whole-genome alignment

Whole-Genome Comparative genomics: Evolutionary signatures, Duplication

Genome Assembly: Consensus-alignment-overlap, Graph-based assembly

Hidden Markov Models Part 1: Evaluation / Parsing, Viterbi/Forward algorithm

Hidden Markov Models Part 2: PosteriorDecoding / Learning Baum Welch

Structural RNAs: Fold prediction, genome-wide annotation

Transcript structure analysis, Differential Expression, Significance Testing

Small and Large regulatory RNAs: lincRNA, miRNA, piRNA

Expression analysis: Clustering and Classification, K-means, Naïve-Bayes

Clustering: affinity propagation; Classification: Random Forests

Epigenomics: ChIP-Seq, Burrows-Wheeler alignment, Chromatin States

Regulatory Motifs: Discovery, Representation, PBMs, Gibbs Sampling, EM

Expression deconvolution functional data of mixed samples

Network Inference: Physical and functional networks, information integration

The ENCODE Project: Systematic experimentation and integrative genomics

Dimentionality reduction, PCA, Self-Organizing Maps, SVMs

Disease association mapping, GWAS, organismal phenotypes

Quantitative trait mapping, eQTLs, molecular trait variation

Linkage Disequilibrium, Haplotype phasing, variant imputat

Molecular Evolution, Tree Building, Phylogenetic inference

Gene/species trees, reconciliation, recombination graphs

Mutation rate estimation, coalescent, models of evolution

Missing Heritability, Complex Traits, Interpret GWAS, Rank-based enrichment

Recent human evolution: Human history, human selection

Personal Genomics, Disease Epigenomics: Systems approaches to disease

Three-dimentional chromatin interactions: 3C, 5C, HiC, ChIA-Pet

Pharmacogenomics: Network-based systems biology of drug response

Synthetic Biology: Reading and writing genomes and cellular circuits

+ Final project

### Textbook list:

No textbook; Kellis was formulating extensive class notes with the help of the students.

## Stanford Course Info

### Description:

Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing; functional landscape of the human genome (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures on current genomic research topics.

### Syllabus:

Introductory biology

Protein Coding Genes

UCSC Genome Browser Tutorial

Introduction to Text Processing Tutorial

Non-protein Coding Genes

Transcriptional Activation I

Transcriptional Regulation II

Transcriptional Regulation III

Genome Evolution I: Repeats

Genome Evolution II

Chains & Nets, Conservation & Function

Sequencing, Human Variation, and Disease

Personal Genomics, GSEA/GREAT

Transcription factor binding sites - Functions and Complexes

Population Genetics & Evo-Devo

Ancestral genome-phenotype mapping

Protein Coding Genes

UCSC Genome Browser Tutorial

Introduction to Text Processing Tutorial

Non-protein Coding Genes

Transcriptional Activation I

Transcriptional Regulation II

Transcriptional Regulation III

Genome Evolution I: Repeats

Genome Evolution II

Chains & Nets, Conservation & Function

Sequencing, Human Variation, and Disease

Personal Genomics, GSEA/GREAT

Transcription factor binding sites - Functions and Complexes

Population Genetics & Evo-Devo

Ancestral genome-phenotype mapping

### Textbook list:

No specific textbook

## General Info

### Stanford course to be waived

: CS 161### Advisor name

: Serge Plotkin## Non-Stanford Course Info

### University Name

: Massachusetts Institute of Technology### Instructor

: Bruce Tidor, Dana Moshkovitz### Year

: 2012### Course website

: http://stellar.mit.edu/S/course/6/sp12/6.046/index.html### Link to course assignments

: http://stellar.mit.edu/S/course/6/sp12/6.046/materials.html (assignments not public)### Description:

Techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics include sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; greedy algorithms; amortized analysis; graph algorithms; and shortest paths. Advanced topics may include network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.

(This is the second algorithms class in the standard sequence)

(This is the second algorithms class in the standard sequence)

### Syllabus:

Median Finding

Scheduling

Minimum Spanning Trees

Fast Fourier Transform

All-Pairs Shortest Paths

Randomized algorithms, high probability bounds

Hashing

Amortized Analysis

Competitive Analysis

Network Flow

van Emde Boas Data Structure

Disjoint Sets Data Structures

P vs. NP

Approximation Algorithms

Compression

Sub-linear time algorithms

Clustering

Derandomization

Computational Geometry

Scheduling

Minimum Spanning Trees

Fast Fourier Transform

All-Pairs Shortest Paths

Randomized algorithms, high probability bounds

Hashing

Amortized Analysis

Competitive Analysis

Network Flow

van Emde Boas Data Structure

Disjoint Sets Data Structures

P vs. NP

Approximation Algorithms

Compression

Sub-linear time algorithms

Clustering

Derandomization

Computational Geometry

### Textbook list:

Introduction to Algorithms (CLRS)

## Stanford Course Info

### Description:

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths.

### Syllabus:

Algorithmic complexity and analysis (4 lectures)

Randomization, divide and conquer (2 lectures)

Heaps and counting sort (1 lecture)

Hashing (2 lectures)

Tree and graph definitions and properties (1 lecture)

Binary Search Trees (1 lecture)

Greedy Algorithms (2 lectures)

Dynamic programming (3 lectures)

Graph algorithms (4 lectures)

Randomization, divide and conquer (2 lectures)

Heaps and counting sort (1 lecture)

Hashing (2 lectures)

Tree and graph definitions and properties (1 lecture)

Binary Search Trees (1 lecture)

Greedy Algorithms (2 lectures)

Dynamic programming (3 lectures)

Graph algorithms (4 lectures)

### Textbook list:

CLRS

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