Continuing from my last blog post, in this blog post I will cover the main topics of other three core classes of UNR MBA, Finance and Economy.
There are two mandatory finance courses in UNR MBA core classes, Financial Reporting and Analysis and Financial Management. You probably heard about Financial Statements somewhere in your undergrad classes or on the news. During the first class, Financial Reporting and Analysis, you will get the better understanding of financial statements and their necessity. You will learn how to create a financial statements and how to extract financial data from the internet, like Yahoo Finance of Google Finance. You need to have a financial calculator in your pocket, and an excel file open on your computer any time you are going to this class.
Financial Management is the complementary course for the former one (although it’s not pre-requisite). Everything in this course is about financial comparison; comparison of two companies within the same industry, comparison of two industries, or even comparison of a company with itself over the specific period of time. You will learn how to calculate financial ratios and their concepts as a tool of comparing two financial units. Maybe you’ll figure out how companies play with number to show they have a better financial position :).
Our only economic core class at UNR MBA is Economics of the firm, which can be summarized in two words, Supply and Demand. Explained by google translate, supply and demand is “the amount of a commodity, product, or service available and the desire of buyers for it, considered as factors regulating its price.” You will learn how supply and demand is effected by in market and how companies earn money in each market. You will also learn about the basic concepts of Game Theory.
Check my next blog post if you want to know about breath and elective classes. By the way, did I say you can contact Dr. Raffiee, graduate director of MBA for any additional questions?
If you read my last blog post, you would probably know what you can get out of your MBA at University of Nevada, Reno (UNR). But there are still some people (like almost all of my engineering colleagues) who want to know what they achieve from MBA classes. Here in this post and couple of next ones, I explain more about the core courses of MBA program at UNR. The total of 51 credits (17 three credits class) is required to graduate from UNR MBA program including: (UNR MBA Curriculum)
- 7 core classes with the total of 21 credits
- 6 breath classes with the total of 18 credits
- 3 elective classes with the total of 9 credits.
Core classes are mandatory courses and include Marketing, Statistics, Organization Behavior, Finance, and economy courses. Here is a short summary for each of these classes.
Statistic gives you the basic tool to understand and implement the math part of all other MBA courses. If you got your bachelor in engineering or any relevant majors, this course would be waived for you with graduate director’s permission.
Marketing is about the understanding of Marketing 4 Ps and investing optimally in each of these Ps to boost your business. For this class you are required to analyses and present two marketing approaches of two published companies. You will also learn about Mark-ups and lots of other good things in this class.
Organization Behavior can be summarized in one equation; People’s behavior is the function of their personality and environment. In this course, you will be trained to understand how you can change people’s behavior with changing their environment. Culture and leadership are the two most important concepts in this lesson. If you want to know more, you should visit Dr. Bret Simmons website.
In my next post, I will explain more about Finance and economy courses. Meanwhile, message me on LinkedIn if you have any questions.
After two years of hard work, I am getting my MBA degree from University of Nevada, Reno this summer. The other day, I was thinking about the overall things that I have learned from my MBA and I found it interesting to share it with you. I would classify all MBA materials in two categories of behavioral concepts and analysis. In other words, each course includes some percent of analysis and some percent of behavioral concepts. After two years of studying in business department, I have tools to analysis a situation and come up with an optimum solution.
Yes! The optimum solution. The optimum solution is a solution that fixes a problem for a long term advantage without hurting other units of the business, and in a larger scale, the total environment. Optimum solution is about finding win-win solutions. I believe that a MBA student is able to measure, analyses, and implement.
Measure the strengths and weaknesses of a company both internally and externally. This measure should consider culture of the company, governmental regulations, geographic location, financial positions, stakeholders’ value, past tries and errors, economical situations, and etc., as well as competitor’s positions.
Analysis of the impact of any decisions or strategies on a short and long-term period. When an MBA student models an organization as a system, he (she) models it as a complex system with hundreds of input and probably couple of outputs in a dynamic environment.
Implementation of a new decision or strategy. This would be the hardest role of a MBA student and it’s even harder to explain it in words. New strategy always come along with changing culture and a new culture needs the big change in environment to change almost all employees’ behavior. I think no change is even better than bad implementation of a good change.
With having said that, I will give you more information about the micro-level of MBA, which is about MBA courses, at UNR. Please shoot me a message if you have any further questions.
Continuing from my last blog post, the next question that it needs to be answered about learning classifier systems is rule discovery. As I mentioned, the LCS is a set of rules that each rule includes a Condition/ action parts. Explained in the book “Learning Classifier Systems”, random generation of rules can only match with smallest number of problems. The use of past experience to generate random rules is the most effective way to include poorly-understood environments.
The foundation of classifier systems is the set of rules. Generally, the condition part of a rule “looks for” certain condition of the environment, and action part specifies the message to be sent to the environment based on the condition part. “IF there is (a message from the detectors indicating) an object left of center in the field of vision, THEN (by issuing a message to the effectors) cause the eyes to look left.” In computer language, each rule is a message that is represented by a string of bits. Each bit is defined by 0, 1 or #, which is equivalent for true, false, and not care. The genetic algorithm mates these strings to create new rules. Explained by Richard J. Bauer’s book, Learning classifier systems are classified as a reinforcement learning (RL) method and defined in the following setting.
Although this method of RL has been working great to solve some basic problems but it encountered difficulties in complex environment. Probably that’s was new classifier systems were introduced over time (i.e. XCS, TCS, and etc). Check my next blog posts if you want to know more.
Anytime I am talking to people about Machine learning and Artificial Intelligence and I mention “classifier systems”, there are always some people who are surprisingly ask, “what”? If you are one of those people who have never heard about classifier system as a method of learning, you should probably need to read couple of my upcoming posts.
Learning classifier system (LCS) is the first generation of classifier systems that was introduced by J. Holland in 1976. LCS is rule-based machine learning method that combines discovery and learning components and it founds its application in robotics, data mining, and data-driven prediction. From 1990, many different classifier system models (Like XCS, TCS, and ZCS ) were presented to solve real-word problems. Explained by John Holland, classifiers systems are:
- Condition/ action (If-Then) rules, simply a broadcast language
- Using genetic algorithm to learn
- Viewing environment by convey it to the set of detectors
- Acting upon an environment by detectors as well (packet of information, messages)
- Using messages for internal processing
- Learning through measure of their performance on the environment (feedback or payoff)
Moreover, there are three important factors regarding the mechanism of classifier systems;
“Parallelism and coordination are addressed by restricting rule action to the emission of messages.” In other words, set of rules are employed to act upon an environment based on the specific current state of the environment.
“Deciding which rules in a rule-based system are responsible for its successes, particularly when long sequences of ‘stage-setting’ actions precede success, is an interesting and difficult problem” which is defined as Credit assignment. In classifier systems, each rule bids to become active and then stands to profit from bids of subsequent bidders. In classifier systems, credit is accumulated by the rule as strength.
This was a very short introduction for classifiers systems. Please check my next blog posts if you want to know more. I will give you a short summary of the book “Learning Classifier Systems“.
Continuing from my last blog post, there are two more steps to finish the training of the system based on first example, as follows:
2. Updating examined rules: In the second step we will update the strength of rules based on their accuracy. In this example the strength of the rule #2 will be increased from 25 to 35 and the strength of the rule #5 will be decreased from 25 to 15.
3. Modifying set of rules: In this step we will update rules. In this example we will withdraw the rule #5 and replace it with new random rule, shown blow.
The old rule #5 will be replaced by the new one in the set of rules in intelligent agent. Then, the intelligent agent gets more intelligent. It’s time to apply the second example. Based on this training example:
“The windshield is dry and the distance from the car ahead is fine and the velocity is high and the engine is on and the lighting is sufficient and the temperature high ð only doors are locked (no other action taken)
The example is coded as: 011111: ####1”
- Searching among 11 rules in intelligent agent in order to find rules that their If-part match with the If-part of the example. What rules do you think will be picked?
- Updating examined rules: How would you update the strength of selected rules?
The set of picked and updated rules is shown in the figure below. In the next step (3rd step), weak rules (when a rule’s strength is lower than 25) will be replaced by some random rules.
In real example, the process of training happens hundreds to thousands time. Each time an intelligent agent gets more intelligent by examining random rules. In my next blog post, I will explain more about real systems and how it differs from our simple example. If you have any question for me, don’t hesitate to message here or on my LinkedIn.
As I discussed in my last blog post, I want to train an intelligent agent to improve the driving conditions of a passenger car. In this blog post, I will apply the first training example on the set of 11 rules in intelligent agent. Recall from my last blog post, the first training example is:
“The windshield is wet and the distance from the car ahead is too close and the velocity is low and the engine is on and the lighting is sufficient and the temperature is high ð only wipers are turned on (no other action is taken)
The example is coded as: 100111:1#### ”
Applying first training example:
- Searching among 11 rules in intelligent agent in order to find rules that their If-part match with the If-part of the example. As it shown in the picture below, only If-part of rules #2 and #5 is matched with the example. In other words, rules #2 and #5 have If-part conditions that cover the example condition.
- Validate Then-part of the rule #2 based on the If-part the example.
The windshield is wet and the distance from the car ahead is too close and the velocity is low and the engine is on and the lighting is sufficient and the temperature is highðTurn on wiper
This is the true statement. Turning on wipers is a right decision because windshield is wet.
- Validate Then-part of the rule #5 based on the If-part the example.
The windshield is wet and the distance from the car ahead is too close and the velocity is low and the engine is on and the lighting is sufficient and the temperature is highðIncrease speed
This is the wrong statement. Increasing the speed is wrong because the distance from the car ahead is too close.
Please check my next blog post to learn about steps 2 and 3 of training procedure. Meanwhile, if you have any question, please message me here or on my LinkedIn.